import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import warnings
warnings.filterwarnings("ignore")
from pmdarima import auto_arima
cols = ["Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)",
"Total Energy Consumption, Manufacturing(10000 tons of SCE)",
"Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)",
"Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)"]
Arima_Results = []
for col in cols:
Target_Column = col
df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Consumption by Sector.xls")
df.set_index("Databaseï¼Annual",inplace = True)
new_df = df.T[["Indicators",Target_Column]].reset_index(drop = True)
new_df = new_df.rename(columns={"Indicators": "Year"}).sort_values(by="Year")
new_df["Year"] = new_df["Year"].astype("int")
new_df.dropna(inplace = True)
# Generate synthetic time series data
t = new_df["Year"]
y = new_df[Target_Column]
# Find the best ARIMA model using auto_arima
stepwise_fit = auto_arima(y, seasonal=False, trace=True)
# Fit the best ARIMA model
best_order = stepwise_fit.get_params()["order"]
model = stepwise_fit.fit(y)
# Forecast the next 10 data points
forecast, conf_int = model.predict(n_periods=10, return_conf_int=True)
# Append the forecasted values to the original data
extended_t = np.concatenate((t, np.arange(t.iloc[-1]+1, t.iloc[-1]+11)))
extended_y = np.concatenate((y, forecast))
Result = pd.DataFrame({"Year":extended_t,Target_Column:extended_y})
Arima_Results.append(Result)
# Plot original data and forecasted values
plt.figure(figsize=(10, 6))
# Plot original data in a solid line
plt.plot(t, y, color="dodgerblue", linewidth=2, label="Original Data")
# Plot forecasted values as a dashed line with shaded confidence interval
plt.plot(extended_t[-10:], forecast, linestyle="dashed", color="darkorange", label="Forecasted Values")
plt.fill_between(extended_t[-10:], conf_int[:, 0], conf_int[:, 1], color="darkorange", alpha=0.1)
# Set labels and title
plt.xlabel("Year")
plt.ylabel(Target_Column)
plt.title(f"ARIMA Forecast (Order: {best_order})")
# Add grid lines
plt.grid(True)
# Add legend
plt.legend()
# Show the plot
plt.show()
Final_Arima_Results1 = pd.concat(Arima_Results, axis=1)
Final_Arima_Results1.columns.values[0] = "year"
# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_Arima_Results1.columns if col == "Year"][1:]
Final_Arima_Results1.drop(columns=cols_to_drop, inplace=True)
Final_Arima_Results1
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.07 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=236.689, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=235.659, Time=0.03 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=236.015, Time=0.03 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=245.245, Time=0.01 sec ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=237.132, Time=0.04 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=237.491, Time=0.06 sec ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=inf, Time=0.06 sec ARIMA(1,1,0)(0,0,0)[0] : AIC=236.340, Time=0.02 sec Best model: ARIMA(1,1,0)(0,0,0)[0] intercept Total fit time: 0.330 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=340.697, Time=0.04 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=353.277, Time=0.00 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=346.962, Time=0.03 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=347.049, Time=0.05 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=369.994, Time=0.01 sec ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=341.373, Time=0.04 sec ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=340.023, Time=0.03 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=340.000, Time=0.02 sec ARIMA(0,1,2)(0,0,0)[0] intercept : AIC=349.580, Time=0.02 sec ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=341.371, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] : AIC=339.532, Time=0.02 sec ARIMA(0,1,1)(0,0,0)[0] : AIC=363.554, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] : AIC=366.773, Time=0.01 sec ARIMA(2,1,1)(0,0,0)[0] : AIC=341.132, Time=0.03 sec ARIMA(1,1,2)(0,0,0)[0] : AIC=340.978, Time=0.04 sec ARIMA(0,1,2)(0,0,0)[0] : AIC=359.841, Time=0.01 sec ARIMA(2,1,0)(0,0,0)[0] : AIC=351.243, Time=0.01 sec ARIMA(2,1,2)(0,0,0)[0] : AIC=340.287, Time=0.09 sec Best model: ARIMA(1,1,1)(0,0,0)[0] Total fit time: 0.482 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.10 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=234.133, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=235.097, Time=0.02 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=234.723, Time=0.03 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=246.012, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=236.664, Time=0.05 sec Best model: ARIMA(0,1,0)(0,0,0)[0] intercept Total fit time: 0.208 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.09 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=250.867, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=251.893, Time=0.03 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=251.647, Time=0.04 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=268.000, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=253.642, Time=0.04 sec Best model: ARIMA(0,1,0)(0,0,0)[0] intercept Total fit time: 0.213 seconds
| year | Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE) | Total Energy Consumption, Manufacturing(10000 tons of SCE) | Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE) | Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE) | |
|---|---|---|---|---|---|
| 0 | 2003 | 5683.210000 | 111222.870000 | 1098.680000 | 4723.400000 |
| 1 | 2004 | 6391.860000 | 136407.850000 | 1316.210000 | 5498.790000 |
| 2 | 2005 | 6860.460000 | 158234.920000 | 1517.970000 | 5916.630000 |
| 3 | 2006 | 7153.520000 | 174920.090000 | 1886.510000 | 6358.180000 |
| 4 | 2007 | 7068.450000 | 193133.070000 | 2096.360000 | 6731.960000 |
| 5 | 2008 | 6872.630000 | 198406.180000 | 2219.950000 | 6884.910000 |
| 6 | 2009 | 6978.210000 | 206555.600000 | 2228.680000 | 7303.220000 |
| 7 | 2010 | 7266.500000 | 217328.870000 | 2547.390000 | 7847.100000 |
| 8 | 2011 | 7675.230000 | 229090.990000 | 2650.410000 | 9147.500000 |
| 9 | 2012 | 7803.570000 | 234538.810000 | 2689.440000 | 10012.330000 |
| 10 | 2013 | 8054.800000 | 239053.400000 | 2801.590000 | 10598.160000 |
| 11 | 2014 | 8020.000000 | 248976.000000 | 2968.000000 | 10864.000000 |
| 12 | 2015 | 8271.000000 | 248264.000000 | 3149.000000 | 11447.000000 |
| 13 | 2016 | 8585.000000 | 247658.000000 | 3377.000000 | 12042.000000 |
| 14 | 2017 | 8945.000000 | 252462.000000 | 3662.000000 | 12456.000000 |
| 15 | 2018 | 8781.000000 | 258604.000000 | 4628.000000 | 12994.000000 |
| 16 | 2019 | 9018.000000 | 268426.000000 | 5028.000000 | 13624.000000 |
| 17 | 2020 | 9263.000000 | 279651.000000 | 5120.000000 | 13171.000000 |
| 18 | 2021 | 9490.430575 | 283844.625975 | 5356.548235 | 13667.917647 |
| 19 | 2022 | 9710.376335 | 287419.303954 | 5593.096471 | 14164.835294 |
| 20 | 2023 | 9927.133462 | 290466.386055 | 5829.644706 | 14661.752941 |
| 21 | 2024 | 10142.532189 | 293063.741505 | 6066.192941 | 15158.670588 |
| 22 | 2025 | 10357.352217 | 295277.746614 | 6302.741176 | 15655.588235 |
| 23 | 2026 | 10571.925713 | 297164.981038 | 6539.289412 | 16152.505882 |
| 24 | 2027 | 10786.394183 | 298773.673701 | 6775.837647 | 16649.423529 |
| 25 | 2028 | 11000.817910 | 300144.935294 | 7012.385882 | 17146.341176 |
| 26 | 2029 | 11215.222576 | 301313.808877 | 7248.934118 | 17643.258824 |
| 27 | 2030 | 11429.619121 | 302310.165414 | 7485.482353 | 18140.176471 |
from pmdarima import auto_arima
cols = ["Total Energy Consumption(10000 tons of SCE)",
"Proportion of Coal(%)",
"Proportion of Petroleum(%)",
"Proportion of Natural Gas(%)",
"Proportion of Primary Electricity and Other Energy(%)",
"Consumption of Coal(10000 tons)",
"Consumption of Coke(10000 tons)",
"Consumption of Crude Oil(10000 tons)",
"Consumption of Gasoline(10000 tons)",
"Consumption of Kerosene(10000 tons)",
"Consumption of Diesel Oil(10000 tons)",
"Consumption of Fuel Oil(10000 tons)",
"Consumption of Natural Gas(100 million cu.m)",
"Consumption of Electricity(100 million kwh)"]
Arima_Results = []
for col in cols:
Target_Column = col
df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Annual Total Energy Consumption.xls")
df.set_index("Databaseï¼Annual",inplace = True)
new_df = df.T[["Indicators",Target_Column]].reset_index(drop = True)
new_df = new_df.rename(columns={"Indicators": "Year"}).sort_values(by="Year")
new_df["Year"] = new_df["Year"].astype("int")
new_df.dropna(inplace = True)
# Generate synthetic time series data
t = new_df["Year"]
y = new_df[Target_Column]
# Find the best ARIMA model using auto_arima
stepwise_fit = auto_arima(y, seasonal=False, trace=True)
# Fit the best ARIMA model
best_order = stepwise_fit.get_params()["order"]
model = stepwise_fit.fit(y)
# Forecast the next 10 data points
forecast, conf_int = model.predict(n_periods=10, return_conf_int=True)
# Append the forecasted values to the original data
extended_t = np.concatenate((t, np.arange(t.iloc[-1]+1, t.iloc[-1]+11)))
extended_y = np.concatenate((y, forecast))
Result = pd.DataFrame({"Year":extended_t,Target_Column:extended_y})
Arima_Results.append(Result)
# Plot original data and forecasted values
plt.figure(figsize=(10, 6))
# Plot original data in a solid line
plt.plot(t, y, color="dodgerblue", linewidth=2, label="Original Data")
# Plot forecasted values as a dashed line with shaded confidence interval
plt.plot(extended_t[-10:], forecast, linestyle="dashed", color="darkorange", label="Forecasted Values")
plt.fill_between(extended_t[-10:], conf_int[:, 0], conf_int[:, 1], color="darkorange", alpha=0.1)
# Set labels and title
plt.xlabel("Year")
plt.ylabel(Target_Column)
plt.title(f"ARIMA Forecast (Order: {best_order})")
# Add grid lines
plt.grid(True)
# Add legend
plt.legend()
# Show the plot
plt.show()
Final_Arima_Results2 = pd.concat(Arima_Results, axis=1)
Final_Arima_Results2.columns.values[0] = "year"
# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_Arima_Results2.columns if col == "Year"][1:]
Final_Arima_Results2.drop(columns=cols_to_drop, inplace=True)
Final_Arima_Results2
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.09 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=398.490, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=400.361, Time=0.04 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=397.281, Time=0.02 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=431.725, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=393.543, Time=0.03 sec ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=392.729, Time=0.02 sec ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=394.462, Time=0.02 sec ARIMA(3,1,1)(0,0,0)[0] intercept : AIC=394.091, Time=0.03 sec ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=392.857, Time=0.03 sec ARIMA(3,1,0)(0,0,0)[0] intercept : AIC=392.635, Time=0.02 sec ARIMA(4,1,0)(0,0,0)[0] intercept : AIC=394.899, Time=0.02 sec ARIMA(4,1,1)(0,0,0)[0] intercept : AIC=393.930, Time=0.04 sec ARIMA(3,1,0)(0,0,0)[0] : AIC=421.137, Time=0.02 sec Best model: ARIMA(3,1,0)(0,0,0)[0] intercept Total fit time: 0.396 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.09 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=62.219, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=63.854, Time=0.01 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=64.030, Time=0.02 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=67.054, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=64.295, Time=0.03 sec Best model: ARIMA(0,1,0)(0,0,0)[0] intercept Total fit time: 0.166 seconds
Performing stepwise search to minimize aic ARIMA(2,0,2)(0,0,0)[0] : AIC=54.025, Time=0.06 sec ARIMA(0,0,0)(0,0,0)[0] : AIC=165.843, Time=0.01 sec ARIMA(1,0,0)(0,0,0)[0] : AIC=inf, Time=0.01 sec ARIMA(0,0,1)(0,0,0)[0] : AIC=inf, Time=0.02 sec ARIMA(1,0,2)(0,0,0)[0] : AIC=51.137, Time=0.04 sec ARIMA(0,0,2)(0,0,0)[0] : AIC=inf, Time=0.04 sec ARIMA(1,0,1)(0,0,0)[0] : AIC=50.766, Time=0.03 sec ARIMA(2,0,1)(0,0,0)[0] : AIC=52.977, Time=0.06 sec ARIMA(2,0,0)(0,0,0)[0] : AIC=inf, Time=0.02 sec ARIMA(1,0,1)(0,0,0)[0] intercept : AIC=45.226, Time=0.04 sec ARIMA(0,0,1)(0,0,0)[0] intercept : AIC=50.487, Time=0.01 sec ARIMA(1,0,0)(0,0,0)[0] intercept : AIC=43.868, Time=0.04 sec ARIMA(0,0,0)(0,0,0)[0] intercept : AIC=60.547, Time=0.00 sec ARIMA(2,0,0)(0,0,0)[0] intercept : AIC=44.762, Time=0.05 sec ARIMA(2,0,1)(0,0,0)[0] intercept : AIC=42.595, Time=0.08 sec ARIMA(3,0,1)(0,0,0)[0] intercept : AIC=42.867, Time=0.11 sec ARIMA(2,0,2)(0,0,0)[0] intercept : AIC=47.985, Time=0.09 sec ARIMA(1,0,2)(0,0,0)[0] intercept : AIC=45.974, Time=0.05 sec ARIMA(3,0,0)(0,0,0)[0] intercept : AIC=43.750, Time=0.08 sec ARIMA(3,0,2)(0,0,0)[0] intercept : AIC=45.851, Time=0.11 sec Best model: ARIMA(2,0,1)(0,0,0)[0] intercept Total fit time: 0.968 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=3.594, Time=0.05 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=-1.885, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=-1.243, Time=0.02 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=-1.804, Time=0.02 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=21.879, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=-0.305, Time=0.04 sec Best model: ARIMA(0,1,0)(0,0,0)[0] intercept Total fit time: 0.147 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=35.646, Time=0.05 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=33.134, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=34.448, Time=0.02 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=34.770, Time=0.02 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=42.538, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=34.154, Time=0.04 sec Best model: ARIMA(0,1,0)(0,0,0)[0] intercept Total fit time: 0.151 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.09 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=380.324, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=388.365, Time=0.01 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=379.255, Time=0.01 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=387.402, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=376.012, Time=0.04 sec ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=380.458, Time=0.02 sec ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=378.612, Time=0.07 sec ARIMA(0,1,2)(0,0,0)[0] intercept : AIC=377.684, Time=0.05 sec ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=380.441, Time=0.02 sec ARIMA(1,1,1)(0,0,0)[0] : AIC=372.715, Time=0.03 sec ARIMA(0,1,1)(0,0,0)[0] : AIC=383.295, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] : AIC=389.508, Time=0.01 sec ARIMA(2,1,1)(0,0,0)[0] : AIC=375.191, Time=0.04 sec ARIMA(1,1,2)(0,0,0)[0] : AIC=389.282, Time=0.03 sec ARIMA(0,1,2)(0,0,0)[0] : AIC=389.545, Time=0.02 sec ARIMA(2,1,0)(0,0,0)[0] : AIC=380.939, Time=0.01 sec ARIMA(2,1,2)(0,0,0)[0] : AIC=inf, Time=0.08 sec Best model: ARIMA(1,1,1)(0,0,0)[0] Total fit time: 0.548 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=304.225, Time=0.05 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=313.431, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=314.887, Time=0.01 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=316.410, Time=0.01 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=321.151, Time=0.01 sec ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=303.768, Time=0.04 sec ARIMA(0,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.06 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=313.863, Time=0.04 sec ARIMA(1,1,3)(0,0,0)[0] intercept : AIC=inf, Time=0.08 sec ARIMA(0,1,3)(0,0,0)[0] intercept : AIC=inf, Time=0.06 sec ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=309.212, Time=0.02 sec ARIMA(2,1,3)(0,0,0)[0] intercept : AIC=inf, Time=0.12 sec ARIMA(1,1,2)(0,0,0)[0] : AIC=inf, Time=0.05 sec Best model: ARIMA(1,1,2)(0,0,0)[0] intercept Total fit time: 0.545 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=292.441, Time=0.08 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=287.882, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=291.635, Time=0.01 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=288.699, Time=0.03 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=320.106, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=289.446, Time=0.02 sec Best model: ARIMA(0,1,0)(0,0,0)[0] intercept Total fit time: 0.144 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.08 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=263.128, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=265.021, Time=0.01 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=265.009, Time=0.02 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=273.420, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=267.132, Time=0.03 sec Best model: ARIMA(0,1,0)(0,0,0)[0] intercept Total fit time: 0.155 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.09 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=234.949, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=236.882, Time=0.02 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=236.849, Time=0.02 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=239.142, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=238.826, Time=0.03 sec Best model: ARIMA(0,1,0)(0,0,0)[0] intercept Total fit time: 0.170 seconds
Performing stepwise search to minimize aic ARIMA(2,2,2)(0,0,0)[0] intercept : AIC=inf, Time=0.09 sec ARIMA(0,2,0)(0,0,0)[0] intercept : AIC=258.111, Time=0.01 sec ARIMA(1,2,0)(0,0,0)[0] intercept : AIC=258.365, Time=0.03 sec ARIMA(0,2,1)(0,0,0)[0] intercept : AIC=inf, Time=0.04 sec ARIMA(0,2,0)(0,0,0)[0] : AIC=256.791, Time=0.01 sec ARIMA(1,2,1)(0,0,0)[0] intercept : AIC=inf, Time=0.06 sec Best model: ARIMA(0,2,0)(0,0,0)[0] Total fit time: 0.232 seconds
Performing stepwise search to minimize aic ARIMA(2,0,2)(0,0,0)[0] : AIC=inf, Time=nan sec ARIMA(0,0,0)(0,0,0)[0] : AIC=354.125, Time=0.01 sec ARIMA(1,0,0)(0,0,0)[0] : AIC=inf, Time=0.02 sec ARIMA(0,0,1)(0,0,0)[0] : AIC=inf, Time=0.02 sec ARIMA(1,0,1)(0,0,0)[0] : AIC=282.725, Time=0.03 sec ARIMA(2,0,1)(0,0,0)[0] : AIC=284.676, Time=0.05 sec ARIMA(1,0,2)(0,0,0)[0] : AIC=284.162, Time=0.07 sec ARIMA(0,0,2)(0,0,0)[0] : AIC=inf, Time=0.03 sec ARIMA(2,0,0)(0,0,0)[0] : AIC=inf, Time=0.03 sec ARIMA(1,0,1)(0,0,0)[0] intercept : AIC=278.587, Time=0.01 sec ARIMA(0,0,1)(0,0,0)[0] intercept : AIC=278.833, Time=0.03 sec ARIMA(1,0,0)(0,0,0)[0] intercept : AIC=276.551, Time=0.01 sec ARIMA(0,0,0)(0,0,0)[0] intercept : AIC=285.993, Time=0.01 sec ARIMA(2,0,0)(0,0,0)[0] intercept : AIC=278.376, Time=0.02 sec ARIMA(2,0,1)(0,0,0)[0] intercept : AIC=279.929, Time=0.08 sec Best model: ARIMA(1,0,0)(0,0,0)[0] intercept Total fit time: 0.484 seconds
Performing stepwise search to minimize aic ARIMA(2,2,2)(0,0,0)[0] intercept : AIC=inf, Time=0.07 sec ARIMA(0,2,0)(0,0,0)[0] intercept : AIC=192.787, Time=0.01 sec ARIMA(1,2,0)(0,0,0)[0] intercept : AIC=194.695, Time=0.02 sec ARIMA(0,2,1)(0,0,0)[0] intercept : AIC=inf, Time=0.03 sec ARIMA(0,2,0)(0,0,0)[0] : AIC=191.180, Time=0.01 sec ARIMA(1,2,1)(0,0,0)[0] intercept : AIC=inf, Time=0.04 sec Best model: ARIMA(0,2,0)(0,0,0)[0] Total fit time: 0.182 seconds
Performing stepwise search to minimize aic ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.08 sec ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=295.657, Time=0.01 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=298.058, Time=0.01 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=297.739, Time=0.01 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=329.393, Time=0.01 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=299.553, Time=0.04 sec Best model: ARIMA(0,1,0)(0,0,0)[0] intercept Total fit time: 0.156 seconds
| year | Total Energy Consumption(10000 tons of SCE) | Proportion of Coal(%) | Proportion of Petroleum(%) | Proportion of Natural Gas(%) | Proportion of Primary Electricity and Other Energy(%) | Consumption of Coal(10000 tons) | Consumption of Coke(10000 tons) | Consumption of Crude Oil(10000 tons) | Consumption of Gasoline(10000 tons) | Consumption of Kerosene(10000 tons) | Consumption of Diesel Oil(10000 tons) | Consumption of Fuel Oil(10000 tons) | Consumption of Natural Gas(100 million cu.m) | Consumption of Electricity(100 million kwh) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2003 | 197083.000000 | 70.200000 | 20.100000 | 2.300000 | 7.400000 | 183760.240000 | 15926.470000 | 25180.720000 | 4118.520000 | 921.61 | 8575.12 | 4330.340000 | 339.08 | 19031.600000 |
| 1 | 2004 | 230281.000000 | 70.200000 | 19.900000 | 2.300000 | 7.600000 | 212161.830000 | 18067.010000 | 29009.310000 | 4695.720000 | 1060.86 | 10206.92 | 4844.760000 | 396.72 | 21971.370000 |
| 2 | 2005 | 261369.000000 | 72.400000 | 17.800000 | 2.400000 | 7.400000 | 243375.440000 | 25105.840000 | 30088.940000 | 4854.910000 | 1076.84 | 10974.94 | 4244.160000 | 466.08 | 24940.320000 |
| 3 | 2006 | 286467.000000 | 72.400000 | 17.500000 | 2.700000 | 7.400000 | 270639.450000 | 28297.760000 | 32245.200000 | 5242.550000 | 1124.74 | 11729.09 | 4471.150000 | 573.32 | 28587.970000 |
| 4 | 2007 | 311442.000000 | 72.500000 | 17.000000 | 3.000000 | 7.500000 | 290410.120000 | 31168.120000 | 34031.600000 | 5519.090000 | 1243.72 | 12492.38 | 4157.490000 | 705.23 | 32711.810000 |
| 5 | 2008 | 320611.000000 | 71.500000 | 16.700000 | 3.400000 | 8.400000 | 300604.940000 | 32120.240000 | 35510.340000 | 6145.520000 | 1294.01 | 13544.94 | 3236.750000 | 812.94 | 34541.350000 |
| 6 | 2009 | 336126.000000 | 71.600000 | 16.400000 | 3.500000 | 8.500000 | 325002.930000 | 36349.970000 | 38128.590000 | 6172.690000 | 1450.49 | 13551.43 | 2828.800000 | 895.20 | 37032.140000 |
| 7 | 2010 | 360648.000000 | 69.200000 | 17.400000 | 4.000000 | 9.400000 | 349008.260000 | 38702.790000 | 42874.550000 | 6956.200000 | 1765.17 | 14699.00 | 3758.020000 | 1080.24 | 41934.490000 |
| 8 | 2011 | 387043.000000 | 70.200000 | 16.800000 | 4.600000 | 8.400000 | 388961.100000 | 42063.280000 | 43965.840000 | 7595.950000 | 1816.72 | 15635.10 | 3662.800000 | 1341.07 | 47000.880000 |
| 9 | 2012 | 402138.000000 | 68.500000 | 17.000000 | 4.800000 | 9.700000 | 411726.900000 | 44805.230000 | 46678.920000 | 8165.900000 | 1956.60 | 16966.04 | 3683.280000 | 1497.00 | 49762.640000 |
| 10 | 2013 | 416913.000000 | 67.400000 | 17.100000 | 5.300000 | 10.200000 | 424425.940000 | 45851.870000 | 48652.150000 | 9366.350000 | 2164.07 | 17150.65 | 3953.970000 | 1705.37 | 54203.410000 |
| 11 | 2014 | 428334.000000 | 65.800000 | 17.300000 | 5.600000 | 11.300000 | 413633.000000 | 46885.000000 | 51596.950000 | 9776.370000 | 2335.42 | 17165.29 | 4355.470000 | 1870.63 | 57829.690000 |
| 12 | 2015 | 434113.000000 | 63.800000 | 18.400000 | 5.800000 | 12.000000 | 399834.000000 | 44059.000000 | 54788.280000 | 11368.460000 | 2663.71 | 17360.31 | 4662.010000 | 1931.75 | 58019.980000 |
| 13 | 2016 | 441492.000000 | 62.200000 | 18.700000 | 6.100000 | 13.000000 | 388820.000000 | 45462.000000 | 57125.930000 | 11866.040000 | 2970.71 | 16839.04 | 4631.040000 | 2078.06 | 61205.090000 |
| 14 | 2017 | 455827.000000 | 60.600000 | 18.900000 | 6.900000 | 13.600000 | 391403.000000 | 43743.000000 | 59402.170000 | 12296.270000 | 3326.36 | 16916.54 | 4887.300000 | 2393.69 | 65913.970000 |
| 15 | 2018 | 471925.000000 | 59.000000 | 18.900000 | 7.600000 | 14.500000 | 397452.000000 | 43717.000000 | 63004.330000 | 13055.300000 | 3653.51 | 16409.56 | 4536.070000 | 2817.09 | 71508.200000 |
| 16 | 2019 | 487488.000000 | 57.700000 | 19.000000 | 8.000000 | 15.300000 | 401915.000000 | 46426.000000 | 67268.270000 | 13627.970000 | 3950.23 | 14917.95 | 4690.340000 | 3059.68 | 74866.120000 |
| 17 | 2020 | 498314.000000 | 56.900000 | 18.800000 | 8.400000 | 15.900000 | 404860.000000 | 48310.000000 | 69477.140000 | 12767.160000 | 3352.10 | 14282.70 | 5364.600000 | 3339.89 | 77620.170000 |
| 18 | 2021 | 524000.000000 | 56.000000 | 18.500000 | 8.900000 | 16.600000 | 406491.730277 | 50528.015420 | 72082.811765 | 13275.903529 | 3495.07 | 13647.45 | 5157.572502 | 3620.10 | 81066.556471 |
| 19 | 2022 | 541000.000000 | 56.200000 | 17.952838 | 9.266666 | 17.111111 | 407893.569320 | 53335.089824 | 74688.483529 | 13784.647059 | 3638.04 | 13012.20 | 4994.840721 | 3900.31 | 84512.942941 |
| 20 | 2023 | 556580.025388 | 55.463158 | 17.406739 | 9.633333 | 17.622222 | 409097.906049 | 55215.970001 | 77294.155294 | 14293.390588 | 3781.01 | 12376.95 | 4866.927121 | 4180.52 | 87959.329412 |
| 21 | 2024 | 571988.562932 | 54.726316 | 16.965344 | 9.999999 | 18.133333 | 410132.566172 | 56755.741081 | 79899.827059 | 14802.134118 | 3923.98 | 11741.70 | 4766.381985 | 4460.73 | 91405.715882 |
| 22 | 2025 | 586886.656961 | 53.989474 | 16.709009 | 10.366665 | 18.644444 | 411021.455088 | 58169.884715 | 82505.498824 | 15310.877647 | 4066.95 | 11106.45 | 4687.349541 | 4740.94 | 94852.102353 |
| 23 | 2026 | 601658.190647 | 53.252632 | 16.680309 | 10.733331 | 19.155555 | 411785.110212 | 59537.760877 | 85111.170588 | 15819.621176 | 4209.92 | 10471.20 | 4625.226922 | 5021.15 | 98298.488824 |
| 24 | 2027 | 616369.458281 | 52.515789 | 16.877203 | 11.099998 | 19.666667 | 412441.175485 | 60888.597142 | 87716.842353 | 16328.364706 | 4352.89 | 9835.95 | 4576.396093 | 5301.36 | 101744.875294 |
| 25 | 2028 | 631042.354450 | 51.778947 | 17.254885 | 11.466664 | 20.177778 | 413004.809026 | 62233.157763 | 90322.514118 | 16837.108235 | 4495.86 | 9200.70 | 4538.013134 | 5581.57 | 105191.261765 |
| 26 | 2029 | 645701.137935 | 51.042105 | 17.735703 | 11.833330 | 20.688889 | 413489.033359 | 63575.407121 | 92928.185882 | 17345.851765 | 4638.83 | 8565.45 | 4507.842615 | 5861.78 | 108637.648235 |
| 27 | 2030 | 660352.852806 | 50.305263 | 18.224979 | 12.199996 | 21.200000 | 413905.036289 | 64916.805262 | 95533.857647 | 17854.595294 | 4781.80 | 7930.20 | 4484.127396 | 6141.99 | 112084.034706 |
| 28 | 2031 | 675001.069343 | 49.568421 | 18.629545 | 12.566663 | 21.711111 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 29 | 2032 | 689647.762238 | 48.831579 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
from keras.models import Sequential
from keras.layers import LSTM, Dense
cols = ["Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)",
"Total Energy Consumption, Manufacturing(10000 tons of SCE)",
"Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)",
"Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)"]
LSTM_Results = []
for col in cols:
# Load data
target_column = col
df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Consumption by Sector.xls")
# Set the index
df.set_index("Databaseï¼Annual", inplace=True)
# Prepare the data
new_df = df.T[["Indicators", target_column]].reset_index(drop=True)
new_df = new_df.rename(columns={"Indicators": "Year"}).sort_values(by="Year")
new_df["Year"] = new_df["Year"].astype("int")
new_df.dropna(inplace=True)
# Select the target column
y = new_df[target_column].values
# Normalize the data
scaler = MinMaxScaler()
y_scaled = scaler.fit_transform(y.reshape(-1, 1))
# Prepare the data for LSTM
look_back = 5 # Number of previous time steps to use for prediction
X = []
y_lstm = []
for i in range(len(y_scaled) - look_back):
X.append(y_scaled[i:(i + look_back)])
y_lstm.append(y_scaled[i + look_back])
X, y_lstm = np.array(X), np.array(y_lstm)
# Reshape the data for LSTM (samples, time steps, features)
X = X.reshape(X.shape[0], X.shape[1], 1)
# Build the LSTM model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
model.add(LSTM(units=50, return_sequences=False))
model.add(Dense(units=1))
model.compile(optimizer="adam", loss="mean_squared_error")
# Train the model
model.fit(X, y_lstm, epochs=100, batch_size=32)
# Forecast the next 10 data points
last_sequence = y_scaled[-look_back:]
forecast_scaled = []
for _ in range(10):
input_data = np.array([last_sequence[-look_back:]])
input_data = input_data.reshape((1, look_back, 1))
forecast_scaled.append(model.predict(input_data)[0, 0])
last_sequence = np.append(last_sequence, forecast_scaled[-1])
# Inverse transform the forecasted data
forecast = scaler.inverse_transform(np.array(forecast_scaled).reshape(-1, 1)).flatten()
# Append the forecasted values to the original data
extended_t = np.concatenate((new_df["Year"], np.arange(new_df["Year"].iloc[-1] + 1, new_df["Year"].iloc[-1] + 11)))
extended_y = np.concatenate((y, forecast))
Result = pd.DataFrame({"Year": extended_t, target_column: extended_y})
LSTM_Results.append(Result)
# Plot original data and forecasted values
plt.figure(figsize=(10, 6))
plt.plot(new_df["Year"], y, color="dodgerblue", linewidth=2, label="Original Data")
plt.plot(extended_t[-10:], forecast, linestyle="dashed", color="darkorange", label="Forecasted Values")
plt.xlabel("Year")
plt.ylabel(target_column)
plt.title("LSTM Forecast")
plt.grid(True)
plt.legend()
plt.show()
Final_LSTM_Results1 = pd.concat(LSTM_Results, axis=1)
Final_LSTM_Results1.columns.values[0] = "year"
# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_LSTM_Results1.columns if col == "Year"][1:]
Final_LSTM_Results1.drop(columns=cols_to_drop, inplace=True)
Final_LSTM_Results1
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.5454 Epoch 2/100 1/1 [==============================] - 0s 3ms/step - loss: 0.5088 Epoch 3/100 1/1 [==============================] - 0s 3ms/step - loss: 0.4738 Epoch 4/100 1/1 [==============================] - 0s 3ms/step - loss: 0.4398 Epoch 5/100 1/1 [==============================] - 0s 5ms/step - loss: 0.4064 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3734 Epoch 7/100 1/1 [==============================] - 0s 3ms/step - loss: 0.3407 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3079 Epoch 9/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2752 Epoch 10/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2425 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2098 Epoch 12/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1775 Epoch 13/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1459 Epoch 14/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1153 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0866 Epoch 16/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0604 Epoch 17/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0381 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0207 Epoch 19/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0098 Epoch 20/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0065 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0110 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0214 Epoch 23/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0334 Epoch 24/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0421 Epoch 25/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0448 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0418 Epoch 27/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0350 Epoch 28/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0267 Epoch 29/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0188 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0127 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0087 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0066 Epoch 34/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0075 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0091 Epoch 36/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0108 Epoch 37/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0124 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0135 Epoch 39/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0142 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0142 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0138 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0129 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0117 Epoch 44/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0104 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0090 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0078 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0069 Epoch 48/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0063 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0060 Epoch 50/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0060 Epoch 51/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0063 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0066 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0070 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0073 Epoch 55/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0074 Epoch 56/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0072 Epoch 57/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0070 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0066 Epoch 59/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0062 Epoch 60/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0058 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0055 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0053 Epoch 63/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0052 Epoch 64/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0052 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0052 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0053 Epoch 67/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0053 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0053 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0053 Epoch 70/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0052 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0051 Epoch 72/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0049 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0047 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0046 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0045 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0044 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0044 Epoch 79/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0044 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0044 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0044 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0043 Epoch 83/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0043 Epoch 84/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0042 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0040 Epoch 87/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0040 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0039 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0039 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0039 Epoch 91/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0038 Epoch 92/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0038 Epoch 93/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0038 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0037 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0037 Epoch 96/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0037 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0036 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0036 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0035 Epoch 100/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0035 1/1 [==============================] - 0s 463ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.5454 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.5009 Epoch 3/100 1/1 [==============================] - 0s 3ms/step - loss: 0.4572 Epoch 4/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4141 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3716 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3296 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2881 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2473 Epoch 9/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2072 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1683 Epoch 11/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1312 Epoch 12/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0965 Epoch 13/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0653 Epoch 14/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0389 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0188 Epoch 16/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0065 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0034 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0093 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0216 Epoch 20/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0347 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0435 Epoch 22/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0455 Epoch 23/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0415 Epoch 24/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0336 Epoch 25/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0243 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0158 Epoch 27/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0091 Epoch 28/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0048 Epoch 29/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0028 Epoch 30/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0026 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0036 Epoch 32/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0053 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0071 Epoch 34/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0087 Epoch 35/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0099 Epoch 36/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0106 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0106 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0101 Epoch 39/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0092 Epoch 40/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0080 Epoch 41/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0066 Epoch 42/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0052 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0039 Epoch 44/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0029 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0023 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0020 Epoch 47/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0020 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0022 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0026 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0030 Epoch 51/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0034 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0035 Epoch 53/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0035 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0033 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0030 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0027 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0023 Epoch 58/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0020 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0018 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0017 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0017 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0017 Epoch 63/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0018 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0019 Epoch 65/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0020 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0020 Epoch 67/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0020 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0020 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0019 Epoch 70/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0018 Epoch 71/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0017 Epoch 72/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0016 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0016 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 78/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0016 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0016 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0016 Epoch 81/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0016 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 87/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 91/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 93/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 95/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0014 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 99/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0013 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 1/1 [==============================] - 0s 447ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step
Epoch 1/100 1/1 [==============================] - 3s 3s/step - loss: 0.3350 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3080 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2816 Epoch 4/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2557 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2302 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2049 Epoch 7/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1800 Epoch 8/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1555 Epoch 9/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1316 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1086 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0867 Epoch 12/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0665 Epoch 13/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0485 Epoch 14/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0335 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0223 Epoch 16/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0157 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0143 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0179 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0249 Epoch 20/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0325 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0377 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0391 Epoch 23/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0368 Epoch 24/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0322 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0267 Epoch 26/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0215 Epoch 27/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0173 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0146 Epoch 29/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0132 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0130 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0135 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0144 Epoch 33/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0155 Epoch 34/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0164 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0171 Epoch 36/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0174 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0173 Epoch 38/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0168 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0160 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0150 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0140 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0129 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0120 Epoch 44/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0113 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0108 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0106 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0106 Epoch 48/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0107 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0108 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0110 Epoch 51/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0110 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0108 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0105 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0102 Epoch 55/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0097 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0093 Epoch 57/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0090 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0087 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0085 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0084 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0083 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0083 Epoch 63/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0082 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0081 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0080 Epoch 66/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0078 Epoch 67/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0076 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0074 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0072 Epoch 70/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0070 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0069 Epoch 72/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0067 Epoch 74/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0066 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0066 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0065 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0064 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0063 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0062 Epoch 80/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0061 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0060 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0059 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0058 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0058 Epoch 85/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0057 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0057 Epoch 87/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0056 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0056 Epoch 89/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0055 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0055 Epoch 91/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0054 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0054 Epoch 93/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0053 Epoch 94/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0053 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0052 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0052 Epoch 97/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0052 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0052 Epoch 99/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0052 Epoch 100/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0051 1/1 [==============================] - 0s 432ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.4767 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4441 Epoch 3/100 1/1 [==============================] - 0s 3ms/step - loss: 0.4124 Epoch 4/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3812 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3503 Epoch 6/100 1/1 [==============================] - 0s 3ms/step - loss: 0.3194 Epoch 7/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2886 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2576 Epoch 9/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2266 Epoch 10/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1956 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1650 Epoch 12/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1350 Epoch 13/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1060 Epoch 14/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0788 Epoch 15/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0542 Epoch 16/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0333 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0172 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0074 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0050 Epoch 20/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0098 Epoch 21/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0200 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0311 Epoch 23/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0387 Epoch 24/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0406 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0374 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0308 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0231 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0159 Epoch 29/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0103 Epoch 30/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0068 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0051 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0050 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0059 Epoch 34/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0074 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0090 Epoch 36/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0104 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0115 Epoch 38/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0120 Epoch 39/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0121 Epoch 40/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0116 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0108 Epoch 42/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0097 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0085 Epoch 44/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0073 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0063 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0054 Epoch 47/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0049 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0047 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0051 Epoch 51/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0054 Epoch 52/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0058 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0060 Epoch 54/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0061 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0061 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0059 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0056 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0052 Epoch 59/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0049 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0047 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0045 Epoch 62/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0045 Epoch 63/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0045 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0045 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0046 Epoch 66/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0047 Epoch 67/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0047 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0047 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0047 Epoch 70/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0047 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0046 Epoch 72/100 1/1 [==============================] - 0s 8ms/step - loss: 0.0045 Epoch 73/100 1/1 [==============================] - 0s 7ms/step - loss: 0.0044 Epoch 74/100 1/1 [==============================] - 0s 7ms/step - loss: 0.0043 Epoch 75/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0043 Epoch 76/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0043 Epoch 77/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0043 Epoch 78/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0043 Epoch 79/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0043 Epoch 80/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0043 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0043 Epoch 82/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0043 Epoch 83/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0043 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0042 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0042 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0042 Epoch 87/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0042 Epoch 88/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0041 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 Epoch 91/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 Epoch 93/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0041 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 Epoch 95/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0041 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 Epoch 98/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0041 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0041 1/1 [==============================] - 0s 432ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step
| year | Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE) | Total Energy Consumption, Manufacturing(10000 tons of SCE) | Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE) | Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE) | |
|---|---|---|---|---|---|
| 0 | 2003 | 5683.210000 | 111222.87000 | 1098.680000 | 4723.400000 |
| 1 | 2004 | 6391.860000 | 136407.85000 | 1316.210000 | 5498.790000 |
| 2 | 2005 | 6860.460000 | 158234.92000 | 1517.970000 | 5916.630000 |
| 3 | 2006 | 7153.520000 | 174920.09000 | 1886.510000 | 6358.180000 |
| 4 | 2007 | 7068.450000 | 193133.07000 | 2096.360000 | 6731.960000 |
| 5 | 2008 | 6872.630000 | 198406.18000 | 2219.950000 | 6884.910000 |
| 6 | 2009 | 6978.210000 | 206555.60000 | 2228.680000 | 7303.220000 |
| 7 | 2010 | 7266.500000 | 217328.87000 | 2547.390000 | 7847.100000 |
| 8 | 2011 | 7675.230000 | 229090.99000 | 2650.410000 | 9147.500000 |
| 9 | 2012 | 7803.570000 | 234538.81000 | 2689.440000 | 10012.330000 |
| 10 | 2013 | 8054.800000 | 239053.40000 | 2801.590000 | 10598.160000 |
| 11 | 2014 | 8020.000000 | 248976.00000 | 2968.000000 | 10864.000000 |
| 12 | 2015 | 8271.000000 | 248264.00000 | 3149.000000 | 11447.000000 |
| 13 | 2016 | 8585.000000 | 247658.00000 | 3377.000000 | 12042.000000 |
| 14 | 2017 | 8945.000000 | 252462.00000 | 3662.000000 | 12456.000000 |
| 15 | 2018 | 8781.000000 | 258604.00000 | 4628.000000 | 12994.000000 |
| 16 | 2019 | 9018.000000 | 268426.00000 | 5028.000000 | 13624.000000 |
| 17 | 2020 | 9263.000000 | 279651.00000 | 5120.000000 | 13171.000000 |
| 18 | 2021 | 9470.599609 | 272715.62500 | 5903.904785 | 14345.327148 |
| 19 | 2022 | 9644.968750 | 277320.56250 | 6824.806152 | 14737.408203 |
| 20 | 2023 | 9760.621094 | 281679.71875 | 7970.797363 | 15138.805664 |
| 21 | 2024 | 9985.249023 | 285492.00000 | 9044.349609 | 15512.595703 |
| 22 | 2025 | 10194.104492 | 287971.68750 | 10409.479492 | 15828.724609 |
| 23 | 2026 | 10384.377930 | 288766.31250 | 12293.153320 | 16377.260742 |
| 24 | 2027 | 10567.852539 | 291450.31250 | 14324.307617 | 16724.646484 |
| 25 | 2028 | 10755.415039 | 293692.59375 | 16386.732422 | 17069.849609 |
| 26 | 2029 | 10966.658203 | 295504.28125 | 18308.341797 | 17407.250000 |
| 27 | 2030 | 11167.495117 | 296954.62500 | 20062.207031 | 17740.669922 |
from keras.models import Sequential
from keras.layers import LSTM, Dense
cols = ["Total Energy Consumption(10000 tons of SCE)",
"Proportion of Coal(%)",
"Proportion of Petroleum(%)",
"Proportion of Natural Gas(%)",
"Proportion of Primary Electricity and Other Energy(%)",
"Consumption of Coal(10000 tons)",
"Consumption of Coke(10000 tons)",
"Consumption of Crude Oil(10000 tons)",
"Consumption of Gasoline(10000 tons)",
"Consumption of Kerosene(10000 tons)",
"Consumption of Diesel Oil(10000 tons)",
"Consumption of Fuel Oil(10000 tons)",
"Consumption of Natural Gas(100 million cu.m)",
"Consumption of Electricity(100 million kwh)"]
LSTM_Results = []
for col in cols:
# Load data
target_column = col
df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Annual Total Energy Consumption.xls")
# Set the index
df.set_index("Databaseï¼Annual", inplace=True)
# Prepare the data
new_df = df.T[["Indicators", target_column]].reset_index(drop=True)
new_df = new_df.rename(columns={"Indicators": "Year"}).sort_values(by="Year")
new_df["Year"] = new_df["Year"].astype("int")
new_df.dropna(inplace=True)
# Select the target column
y = new_df[target_column].values
# Normalize the data
scaler = MinMaxScaler()
y_scaled = scaler.fit_transform(y.reshape(-1, 1))
# Prepare the data for LSTM
look_back = 5 # Number of previous time steps to use for prediction
X = []
y_lstm = []
for i in range(len(y_scaled) - look_back):
X.append(y_scaled[i:(i + look_back)])
y_lstm.append(y_scaled[i + look_back])
X, y_lstm = np.array(X), np.array(y_lstm)
# Reshape the data for LSTM (samples, time steps, features)
X = X.reshape(X.shape[0], X.shape[1], 1)
# Build the LSTM model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
model.add(LSTM(units=50, return_sequences=False))
model.add(Dense(units=1))
model.compile(optimizer="adam", loss="mean_squared_error")
# Train the model
model.fit(X, y_lstm, epochs=100, batch_size=32)
# Forecast the next 10 data points
last_sequence = y_scaled[-look_back:]
forecast_scaled = []
for _ in range(10):
input_data = np.array([last_sequence[-look_back:]])
input_data = input_data.reshape((1, look_back, 1))
forecast_scaled.append(model.predict(input_data)[0, 0])
last_sequence = np.append(last_sequence, forecast_scaled[-1])
# Inverse transform the forecasted data
forecast = scaler.inverse_transform(np.array(forecast_scaled).reshape(-1, 1)).flatten()
# Append the forecasted values to the original data
extended_t = np.concatenate((new_df["Year"], np.arange(new_df["Year"].iloc[-1] + 1, new_df["Year"].iloc[-1] + 11)))
extended_y = np.concatenate((y, forecast))
Result = pd.DataFrame({"Year": extended_t, target_column: extended_y})
LSTM_Results.append(Result)
# Plot original data and forecasted values
plt.figure(figsize=(10, 6))
plt.plot(new_df["Year"], y, color="dodgerblue", linewidth=2, label="Original Data")
plt.plot(extended_t[-10:], forecast, linestyle="dashed", color="darkorange", label="Forecasted Values")
plt.xlabel("Year")
plt.ylabel(target_column)
plt.title("LSTM Forecast")
plt.grid(True)
plt.legend()
plt.show()
Final_LSTM_Results2 = pd.concat(LSTM_Results, axis=1)
Final_LSTM_Results2.columns.values[0] = "year"
# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_LSTM_Results2.columns if col == "Year"][1:]
Final_LSTM_Results2.drop(columns=cols_to_drop, inplace=True)
Final_LSTM_Results2
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.4724 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4347 Epoch 3/100 1/1 [==============================] - 0s 5ms/step - loss: 0.3977 Epoch 4/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3611 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3249 Epoch 6/100 1/1 [==============================] - 0s 5ms/step - loss: 0.2890 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2534 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2181 Epoch 9/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1833 Epoch 10/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1494 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1168 Epoch 12/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0861 Epoch 13/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0583 Epoch 14/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0346 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0161 Epoch 16/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0045 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 9.4742e-04 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0056 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0164 Epoch 20/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0285 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0370 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0393 Epoch 23/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0360 Epoch 24/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0289 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0206 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0128 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0030 Epoch 29/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 31/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0022 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0038 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0056 Epoch 34/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0071 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 36/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0088 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0089 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0084 Epoch 39/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0075 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0064 Epoch 41/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0051 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0038 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0027 Epoch 44/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0018 Epoch 45/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0013 Epoch 46/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0011 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 48/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0015 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0019 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0023 Epoch 51/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0026 Epoch 52/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0027 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0027 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0025 Epoch 55/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0022 Epoch 56/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0018 Epoch 57/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0015 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 61/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0011 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 63/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 67/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0014 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 70/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0012 Epoch 71/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0011 Epoch 72/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 77/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0011 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 84/100 1/1 [==============================] - 0s 3ms/step - loss: 9.9447e-04 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 9.7548e-04 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 9.6420e-04 Epoch 87/100 1/1 [==============================] - 0s 3ms/step - loss: 9.6036e-04 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 9.6212e-04 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 9.6677e-04 Epoch 90/100 1/1 [==============================] - 0s 5ms/step - loss: 9.7150e-04 Epoch 91/100 1/1 [==============================] - 0s 4ms/step - loss: 9.7403e-04 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 9.7307e-04 Epoch 93/100 1/1 [==============================] - 0s 3ms/step - loss: 9.6841e-04 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 9.6079e-04 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 9.5156e-04 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 9.4228e-04 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 9.3429e-04 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 9.2846e-04 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 9.2501e-04 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 9.2359e-04 1/1 [==============================] - 0s 434ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 14ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.3262 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2955 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2661 Epoch 4/100 1/1 [==============================] - 0s 5ms/step - loss: 0.2380 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2110 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1851 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1604 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1371 Epoch 9/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1152 Epoch 10/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0953 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0777 Epoch 12/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0633 Epoch 13/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0526 Epoch 14/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0465 Epoch 15/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0454 Epoch 16/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0489 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0553 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0616 Epoch 19/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0652 Epoch 20/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0651 Epoch 21/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0618 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0567 Epoch 23/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0512 Epoch 24/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0462 Epoch 25/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0426 Epoch 26/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0403 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0392 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0391 Epoch 29/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0395 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0401 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0407 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0409 Epoch 33/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0408 Epoch 34/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0402 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0393 Epoch 36/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0379 Epoch 37/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0364 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0347 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0331 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0317 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0305 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0297 Epoch 43/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0291 Epoch 44/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0287 Epoch 45/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0284 Epoch 46/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0281 Epoch 47/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0276 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0269 Epoch 49/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0260 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0250 Epoch 51/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0239 Epoch 52/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0230 Epoch 53/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0221 Epoch 54/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0214 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0208 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0203 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0198 Epoch 58/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0193 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0186 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0180 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0173 Epoch 62/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0165 Epoch 63/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0158 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0152 Epoch 65/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0146 Epoch 66/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0141 Epoch 67/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0137 Epoch 68/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0132 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0127 Epoch 70/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0122 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0117 Epoch 72/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0112 Epoch 73/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0107 Epoch 74/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0103 Epoch 75/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0100 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0097 Epoch 77/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0093 Epoch 78/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0090 Epoch 79/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0087 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0084 Epoch 81/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0081 Epoch 82/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0078 Epoch 83/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0076 Epoch 84/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0074 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0072 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0070 Epoch 87/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0067 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0065 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0064 Epoch 91/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0063 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0062 Epoch 93/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0061 Epoch 94/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0061 Epoch 95/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0060 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0059 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0059 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0058 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0058 Epoch 100/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0057 1/1 [==============================] - 0s 436ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.2097 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1966 Epoch 3/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1840 Epoch 4/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1719 Epoch 5/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1600 Epoch 6/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1485 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1372 Epoch 8/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1261 Epoch 9/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1154 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1051 Epoch 11/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0953 Epoch 12/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0864 Epoch 13/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0785 Epoch 14/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0719 Epoch 15/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0671 Epoch 16/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0644 Epoch 17/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0639 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0654 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0679 Epoch 20/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0703 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0715 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0713 Epoch 23/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0699 Epoch 24/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0678 Epoch 25/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0654 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0632 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0613 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0600 Epoch 29/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0592 Epoch 30/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0588 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0587 Epoch 32/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0587 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0589 Epoch 34/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0590 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0590 Epoch 36/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0589 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0588 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0584 Epoch 39/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0580 Epoch 40/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0576 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0570 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0565 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0560 Epoch 44/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0556 Epoch 45/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0552 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0549 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0546 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0544 Epoch 49/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0543 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0541 Epoch 51/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0540 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0538 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0535 Epoch 54/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0532 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0529 Epoch 56/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0526 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0522 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0519 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0516 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0513 Epoch 61/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0510 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0507 Epoch 63/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0504 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0501 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0498 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0495 Epoch 67/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0492 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0488 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0484 Epoch 70/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0480 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0476 Epoch 72/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0471 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0467 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0462 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0458 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0453 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0448 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0443 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0437 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0432 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0426 Epoch 82/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0419 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0413 Epoch 84/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0406 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0399 Epoch 86/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0392 Epoch 87/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0385 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0377 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0369 Epoch 90/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0360 Epoch 91/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0351 Epoch 92/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0342 Epoch 93/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0333 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0323 Epoch 95/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0313 Epoch 96/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0303 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0292 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0281 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0270 Epoch 100/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0260 1/1 [==============================] - 0s 439ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.3607 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3376 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3147 Epoch 4/100 1/1 [==============================] - 0s 5ms/step - loss: 0.2919 Epoch 5/100 1/1 [==============================] - 0s 5ms/step - loss: 0.2690 Epoch 6/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2461 Epoch 7/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2229 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1996 Epoch 9/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1761 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1526 Epoch 11/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1292 Epoch 12/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1062 Epoch 13/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0840 Epoch 14/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0630 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0441 Epoch 16/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0279 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0156 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0083 Epoch 19/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0067 Epoch 20/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0109 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0192 Epoch 22/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0278 Epoch 23/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0334 Epoch 24/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0342 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0310 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0253 Epoch 27/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0189 Epoch 28/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0132 Epoch 29/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0090 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0065 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0054 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0056 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0065 Epoch 34/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0077 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0089 Epoch 36/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0099 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0104 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0106 Epoch 39/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0103 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0096 Epoch 41/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0086 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0075 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0063 Epoch 44/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0052 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0043 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0036 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0033 Epoch 48/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0032 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0033 Epoch 50/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0036 Epoch 51/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0038 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0040 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0040 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0038 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0035 Epoch 56/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0031 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0027 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0023 Epoch 59/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0020 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0019 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0018 Epoch 62/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0018 Epoch 63/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0018 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0019 Epoch 65/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0019 Epoch 66/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0018 Epoch 67/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0018 Epoch 68/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0017 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 70/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 71/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0013 Epoch 72/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 73/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0012 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 78/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0012 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 80/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0011 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 9.9206e-04 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 9.8296e-04 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 9.8328e-04 Epoch 87/100 1/1 [==============================] - 0s 3ms/step - loss: 9.8910e-04 Epoch 88/100 1/1 [==============================] - 0s 5ms/step - loss: 9.9598e-04 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 90/100 1/1 [==============================] - 0s 3ms/step - loss: 9.9999e-04 Epoch 91/100 1/1 [==============================] - 0s 3ms/step - loss: 9.9483e-04 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 9.8617e-04 Epoch 93/100 1/1 [==============================] - 0s 5ms/step - loss: 9.7627e-04 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 9.6750e-04 Epoch 95/100 1/1 [==============================] - 0s 3ms/step - loss: 9.6163e-04 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 9.5942e-04 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 9.6050e-04 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 9.6366e-04 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 9.6733e-04 Epoch 100/100 1/1 [==============================] - 0s 5ms/step - loss: 9.7009e-04 1/1 [==============================] - 0s 435ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 10ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step
Epoch 1/100 1/1 [==============================] - 3s 3s/step - loss: 0.3375 Epoch 2/100 1/1 [==============================] - 0s 3ms/step - loss: 0.3163 Epoch 3/100 1/1 [==============================] - 0s 5ms/step - loss: 0.2957 Epoch 4/100 1/1 [==============================] - 0s 5ms/step - loss: 0.2755 Epoch 5/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2556 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2358 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2159 Epoch 8/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1961 Epoch 9/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1762 Epoch 10/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1562 Epoch 11/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1363 Epoch 12/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1165 Epoch 13/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0972 Epoch 14/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0786 Epoch 15/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0612 Epoch 16/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0456 Epoch 17/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0325 Epoch 18/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0227 Epoch 19/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0171 Epoch 20/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0163 Epoch 21/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0200 Epoch 22/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0267 Epoch 23/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0334 Epoch 24/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0374 Epoch 25/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0376 Epoch 26/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0344 Epoch 27/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0292 Epoch 28/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0235 Epoch 29/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0185 Epoch 30/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0146 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0123 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0112 Epoch 33/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0112 Epoch 34/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0117 Epoch 35/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0126 Epoch 36/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0134 Epoch 37/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0140 Epoch 38/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0142 Epoch 39/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0140 Epoch 40/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0134 Epoch 41/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0125 Epoch 42/100 1/1 [==============================] - 0s 7ms/step - loss: 0.0114 Epoch 43/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0101 Epoch 44/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0089 Epoch 45/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0078 Epoch 46/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0070 Epoch 47/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0064 Epoch 48/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0062 Epoch 49/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0062 Epoch 50/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0064 Epoch 51/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0067 Epoch 52/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0068 Epoch 53/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0068 Epoch 54/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0067 Epoch 55/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0064 Epoch 56/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0060 Epoch 57/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0056 Epoch 58/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0053 Epoch 59/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0050 Epoch 60/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0049 Epoch 61/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0050 Epoch 62/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0050 Epoch 63/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0051 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0052 Epoch 65/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0052 Epoch 66/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0052 Epoch 67/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0052 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0051 Epoch 69/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0050 Epoch 70/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0049 Epoch 71/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0048 Epoch 72/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 74/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0048 Epoch 75/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0048 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 77/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0048 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0047 Epoch 81/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0047 Epoch 82/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0047 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0046 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0046 Epoch 85/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0046 Epoch 86/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0046 Epoch 87/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0046 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0046 Epoch 89/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0046 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0046 Epoch 91/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0045 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0045 Epoch 93/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0045 Epoch 94/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0045 Epoch 95/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0045 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0044 Epoch 97/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0044 Epoch 98/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0044 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0044 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0044 1/1 [==============================] - 0s 433ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 10ms/step 1/1 [==============================] - 0s 12ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.7124 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.6657 Epoch 3/100 1/1 [==============================] - 0s 3ms/step - loss: 0.6206 Epoch 4/100 1/1 [==============================] - 0s 3ms/step - loss: 0.5766 Epoch 5/100 1/1 [==============================] - 0s 3ms/step - loss: 0.5334 Epoch 6/100 1/1 [==============================] - 0s 5ms/step - loss: 0.4908 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4486 Epoch 8/100 1/1 [==============================] - 0s 5ms/step - loss: 0.4065 Epoch 9/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3644 Epoch 10/100 1/1 [==============================] - 0s 5ms/step - loss: 0.3223 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2803 Epoch 12/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2387 Epoch 13/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1978 Epoch 14/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1583 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1212 Epoch 16/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0876 Epoch 17/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0591 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0377 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0252 Epoch 20/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0231 Epoch 21/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0311 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0454 Epoch 23/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0596 Epoch 24/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0681 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0688 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0630 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0533 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0425 Epoch 29/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0328 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0254 Epoch 31/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0207 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0185 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0183 Epoch 34/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0193 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0210 Epoch 36/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0228 Epoch 37/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0243 Epoch 38/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0254 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0259 Epoch 40/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0257 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0249 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0237 Epoch 43/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0222 Epoch 44/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0205 Epoch 45/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0189 Epoch 46/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0174 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0162 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0153 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0148 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0146 Epoch 51/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0148 Epoch 52/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0150 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0153 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0156 Epoch 55/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0157 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0156 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0154 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0150 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0145 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0141 Epoch 61/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0136 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0133 Epoch 63/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0130 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0129 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0128 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0128 Epoch 67/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0128 Epoch 68/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0128 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0128 Epoch 70/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0127 Epoch 71/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0126 Epoch 72/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0125 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0124 Epoch 74/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0122 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0120 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0119 Epoch 77/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0118 Epoch 78/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0117 Epoch 79/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0116 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0115 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0115 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0114 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0114 Epoch 84/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0113 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0113 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0112 Epoch 87/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0111 Epoch 88/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0111 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0110 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0109 Epoch 91/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0109 Epoch 92/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0108 Epoch 93/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0107 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0107 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0106 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0106 Epoch 97/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0106 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0105 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0105 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0104 1/1 [==============================] - 0s 433ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.7299 Epoch 2/100 1/1 [==============================] - 0s 3ms/step - loss: 0.6828 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.6374 Epoch 4/100 1/1 [==============================] - 0s 4ms/step - loss: 0.5933 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.5501 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.5075 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4653 Epoch 8/100 1/1 [==============================] - 0s 5ms/step - loss: 0.4231 Epoch 9/100 1/1 [==============================] - 0s 5ms/step - loss: 0.3808 Epoch 10/100 1/1 [==============================] - 0s 5ms/step - loss: 0.3385 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2963 Epoch 12/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2543 Epoch 13/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2128 Epoch 14/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1725 Epoch 15/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1340 Epoch 16/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0985 Epoch 17/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0672 Epoch 18/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0417 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0241 Epoch 20/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0159 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0180 Epoch 22/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0289 Epoch 23/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0438 Epoch 24/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0563 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0621 Epoch 26/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0605 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0532 Epoch 28/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0431 Epoch 29/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0327 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0239 Epoch 31/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0174 Epoch 32/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0136 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0121 Epoch 34/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0123 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0135 Epoch 36/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0153 Epoch 37/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0171 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0186 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0195 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0199 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0197 Epoch 42/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0189 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0177 Epoch 44/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0162 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0146 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0130 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0116 Epoch 48/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0105 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0098 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0094 Epoch 51/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0093 Epoch 52/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0095 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0098 Epoch 54/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0101 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0104 Epoch 56/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0105 Epoch 57/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0104 Epoch 58/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0102 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0098 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0094 Epoch 61/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0090 Epoch 62/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0086 Epoch 63/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0083 Epoch 64/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0081 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0079 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0079 Epoch 67/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0079 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0079 Epoch 69/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0079 Epoch 70/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0079 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0079 Epoch 72/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0078 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0077 Epoch 74/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0076 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0075 Epoch 76/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0073 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0072 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0071 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0070 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0069 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0069 Epoch 82/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0069 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 86/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0067 Epoch 87/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0067 Epoch 88/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0066 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0065 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0065 Epoch 91/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0064 Epoch 92/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0064 Epoch 93/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0063 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0063 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0062 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0062 Epoch 97/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0061 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0061 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0061 Epoch 100/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0060 1/1 [==============================] - 0s 433ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.4005 Epoch 2/100 1/1 [==============================] - 0s 5ms/step - loss: 0.3742 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3481 Epoch 4/100 1/1 [==============================] - 0s 3ms/step - loss: 0.3220 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2959 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2697 Epoch 7/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2433 Epoch 8/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2167 Epoch 9/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1899 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1630 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1362 Epoch 12/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1099 Epoch 13/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0845 Epoch 14/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0608 Epoch 15/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0395 Epoch 16/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0219 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0093 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0030 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0039 Epoch 20/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0112 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0220 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0313 Epoch 23/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0358 Epoch 24/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0346 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0293 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0220 Epoch 27/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0148 Epoch 28/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0088 Epoch 29/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0049 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0029 Epoch 31/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0026 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0034 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 34/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0064 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0079 Epoch 36/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0089 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0095 Epoch 38/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0095 Epoch 39/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0090 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 41/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0070 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0058 Epoch 43/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0045 Epoch 44/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0034 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0026 Epoch 46/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0021 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0020 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0021 Epoch 49/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0024 Epoch 50/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0028 Epoch 51/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0031 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0033 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0033 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0031 Epoch 55/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0028 Epoch 56/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0024 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0020 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0017 Epoch 59/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0014 Epoch 60/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0013 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 63/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0015 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 65/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0015 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 67/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0015 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 70/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0011 Epoch 71/100 1/1 [==============================] - 0s 3ms/step - loss: 9.8189e-04 Epoch 72/100 1/1 [==============================] - 0s 4ms/step - loss: 8.9855e-04 Epoch 73/100 1/1 [==============================] - 0s 5ms/step - loss: 8.4789e-04 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 8.2701e-04 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 8.2697e-04 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 8.3564e-04 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 8.4104e-04 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 8.3434e-04 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 8.1166e-04 Epoch 80/100 1/1 [==============================] - 0s 5ms/step - loss: 7.7431e-04 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 7.2764e-04 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 6.7893e-04 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 6.3510e-04 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 6.0101e-04 Epoch 85/100 1/1 [==============================] - 0s 5ms/step - loss: 5.7845e-04 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 5.6628e-04 Epoch 87/100 1/1 [==============================] - 0s 4ms/step - loss: 5.6120e-04 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 5.5901e-04 Epoch 89/100 1/1 [==============================] - 0s 3ms/step - loss: 5.5577e-04 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 5.4884e-04 Epoch 91/100 1/1 [==============================] - 0s 3ms/step - loss: 5.3725e-04 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 5.2169e-04 Epoch 93/100 1/1 [==============================] - 0s 5ms/step - loss: 5.0404e-04 Epoch 94/100 1/1 [==============================] - 0s 3ms/step - loss: 4.8666e-04 Epoch 95/100 1/1 [==============================] - 0s 3ms/step - loss: 4.7168e-04 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 4.6044e-04 Epoch 97/100 1/1 [==============================] - 0s 3ms/step - loss: 4.5323e-04 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 4.4933e-04 Epoch 99/100 1/1 [==============================] - 0s 5ms/step - loss: 4.4741e-04 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 4.4593e-04 1/1 [==============================] - 0s 434ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step
Epoch 1/100 1/1 [==============================] - 3s 3s/step - loss: 0.4633 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4351 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4082 Epoch 4/100 1/1 [==============================] - 0s 3ms/step - loss: 0.3822 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3569 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3318 Epoch 7/100 1/1 [==============================] - 0s 3ms/step - loss: 0.3069 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2820 Epoch 9/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2570 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2318 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2065 Epoch 12/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1811 Epoch 13/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1558 Epoch 14/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1308 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1063 Epoch 16/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0830 Epoch 17/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0614 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0422 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0265 Epoch 20/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0153 Epoch 21/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0098 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0105 Epoch 23/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0168 Epoch 24/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0263 Epoch 25/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0350 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0397 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0396 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0354 Epoch 29/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0292 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0224 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0166 Epoch 32/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0124 Epoch 33/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0099 Epoch 34/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0089 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0092 Epoch 36/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0101 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0115 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0128 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0139 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0146 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0149 Epoch 42/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0148 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0142 Epoch 44/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0134 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0124 Epoch 46/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0113 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0102 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0093 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0086 Epoch 50/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0082 Epoch 51/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0081 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 53/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0084 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0087 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0090 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0091 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0091 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0090 Epoch 59/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0087 Epoch 60/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0084 Epoch 61/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0081 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0078 Epoch 63/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0076 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0075 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0074 Epoch 66/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0075 Epoch 67/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0075 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0075 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0076 Epoch 70/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0076 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0076 Epoch 72/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0075 Epoch 73/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0074 Epoch 74/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0073 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0072 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0072 Epoch 77/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0071 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0071 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0071 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0071 Epoch 81/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0071 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0071 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0071 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0071 Epoch 85/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0070 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0070 Epoch 87/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0070 Epoch 88/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0069 Epoch 89/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0069 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0069 Epoch 91/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0069 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0069 Epoch 93/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0069 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 98/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0068 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0068 1/1 [==============================] - 0s 433ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.3868 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3607 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3359 Epoch 4/100 1/1 [==============================] - 0s 3ms/step - loss: 0.3120 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2890 Epoch 6/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2665 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2444 Epoch 8/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2226 Epoch 9/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2010 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1795 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1582 Epoch 12/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1372 Epoch 13/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1165 Epoch 14/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0964 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0773 Epoch 16/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0596 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0439 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0308 Epoch 19/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0212 Epoch 20/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0159 Epoch 21/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0152 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0190 Epoch 23/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0258 Epoch 24/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0328 Epoch 25/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0374 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0384 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0359 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0313 Epoch 29/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0259 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0209 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0169 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0143 Epoch 33/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0130 Epoch 34/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0127 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0132 Epoch 36/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0141 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0151 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0160 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0165 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0168 Epoch 41/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0166 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0162 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0154 Epoch 44/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0145 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0134 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0124 Epoch 47/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0115 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0108 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0104 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0102 Epoch 51/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0102 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0103 Epoch 53/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0105 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0107 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0107 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0107 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0105 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0102 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0099 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0095 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0093 Epoch 62/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0091 Epoch 63/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0090 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0089 Epoch 65/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0089 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0090 Epoch 67/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0090 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0090 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0090 Epoch 70/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0089 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0088 Epoch 72/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0087 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0087 Epoch 74/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0086 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0085 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0085 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0085 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0085 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0085 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0085 Epoch 81/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0085 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0084 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0084 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0084 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0083 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0083 Epoch 87/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0083 Epoch 88/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0083 Epoch 89/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0083 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 91/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0082 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 93/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 96/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0082 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0081 Epoch 98/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0081 Epoch 99/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0081 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0081 1/1 [==============================] - 0s 442ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 11ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.6695 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.6249 Epoch 3/100 1/1 [==============================] - 0s 3ms/step - loss: 0.5816 Epoch 4/100 1/1 [==============================] - 0s 4ms/step - loss: 0.5395 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4982 Epoch 6/100 1/1 [==============================] - 0s 3ms/step - loss: 0.4576 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4176 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3779 Epoch 9/100 1/1 [==============================] - 0s 5ms/step - loss: 0.3386 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2995 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2608 Epoch 12/100 1/1 [==============================] - 0s 5ms/step - loss: 0.2228 Epoch 13/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1858 Epoch 14/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1505 Epoch 15/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1176 Epoch 16/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0883 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0639 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0458 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0358 Epoch 20/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0346 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0417 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0539 Epoch 23/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0658 Epoch 24/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0731 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0741 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0694 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0612 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0519 Epoch 29/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0433 Epoch 30/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0364 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0318 Epoch 32/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0294 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0287 Epoch 34/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0293 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0305 Epoch 36/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0320 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0333 Epoch 38/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0343 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0348 Epoch 40/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0347 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0341 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0331 Epoch 43/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0318 Epoch 44/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0303 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0288 Epoch 46/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0274 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0262 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0253 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0247 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0244 Epoch 51/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0244 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0245 Epoch 53/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0247 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0249 Epoch 55/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0250 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0249 Epoch 57/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0247 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0244 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0240 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0235 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0231 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0227 Epoch 63/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0224 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0222 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0221 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0220 Epoch 67/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0219 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0219 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0218 Epoch 70/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0218 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0217 Epoch 72/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0216 Epoch 73/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0214 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0212 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0211 Epoch 76/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0209 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0208 Epoch 78/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0206 Epoch 79/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0205 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0204 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0203 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0202 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0202 Epoch 84/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0201 Epoch 85/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0200 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0199 Epoch 87/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0198 Epoch 88/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0198 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0197 Epoch 90/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0196 Epoch 91/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0195 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0194 Epoch 93/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0193 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0192 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0192 Epoch 96/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0191 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0190 Epoch 98/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0190 Epoch 99/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0189 Epoch 100/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0188 1/1 [==============================] - 0s 443ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 13ms/step
Epoch 1/100 1/1 [==============================] - 3s 3s/step - loss: 0.3368 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3159 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2954 Epoch 4/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2753 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2555 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2359 Epoch 7/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2165 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1974 Epoch 9/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1786 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1603 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1425 Epoch 12/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1258 Epoch 13/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1105 Epoch 14/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0971 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0864 Epoch 16/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0791 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0759 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0770 Epoch 19/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0818 Epoch 20/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0879 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0927 Epoch 22/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0947 Epoch 23/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0936 Epoch 24/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0903 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0860 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0817 Epoch 27/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0781 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0756 Epoch 29/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0741 Epoch 30/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0735 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0737 Epoch 32/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0742 Epoch 33/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0749 Epoch 34/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0756 Epoch 35/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0761 Epoch 36/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0764 Epoch 37/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0765 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0763 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0758 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0752 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0745 Epoch 42/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0738 Epoch 43/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0731 Epoch 44/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0725 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0720 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0717 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0716 Epoch 48/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0716 Epoch 49/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0716 Epoch 50/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0717 Epoch 51/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0718 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0718 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0718 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0716 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0714 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0712 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0710 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0707 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0705 Epoch 60/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0704 Epoch 61/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0703 Epoch 62/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0702 Epoch 63/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0701 Epoch 64/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0701 Epoch 65/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0700 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0700 Epoch 67/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0699 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0698 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0697 Epoch 70/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0696 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0695 Epoch 72/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0693 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0692 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0691 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0690 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0689 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0688 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0688 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0687 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0686 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0685 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0684 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0683 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0682 Epoch 85/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0681 Epoch 86/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0680 Epoch 87/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0679 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0678 Epoch 89/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0677 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0676 Epoch 91/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0675 Epoch 92/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0674 Epoch 93/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0673 Epoch 94/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0672 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0671 Epoch 96/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0670 Epoch 97/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0669 Epoch 98/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0667 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0666 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0665 1/1 [==============================] - 0s 445ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.3305 Epoch 2/100 1/1 [==============================] - 0s 5ms/step - loss: 0.3088 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2876 Epoch 4/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2668 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2461 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2254 Epoch 7/100 1/1 [==============================] - 0s 3ms/step - loss: 0.2046 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1836 Epoch 9/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1625 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1414 Epoch 11/100 1/1 [==============================] - 0s 5ms/step - loss: 0.1205 Epoch 12/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0999 Epoch 13/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0801 Epoch 14/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0615 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0446 Epoch 16/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0303 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0194 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0128 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0113 Epoch 20/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0148 Epoch 21/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0219 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0294 Epoch 23/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0343 Epoch 24/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0351 Epoch 25/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0322 Epoch 26/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0272 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0214 Epoch 28/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0162 Epoch 29/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0123 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0099 Epoch 31/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0088 Epoch 32/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0088 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0095 Epoch 34/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0104 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0114 Epoch 36/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0122 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0126 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0126 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0122 Epoch 40/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0114 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0104 Epoch 42/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0092 Epoch 43/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0081 Epoch 44/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0069 Epoch 45/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0060 Epoch 46/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0053 Epoch 47/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0050 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 49/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0049 Epoch 50/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0050 Epoch 51/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0052 Epoch 52/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0052 Epoch 53/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0051 Epoch 54/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0048 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0044 Epoch 56/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0039 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0034 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0031 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0028 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0026 Epoch 61/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0025 Epoch 62/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0025 Epoch 63/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0025 Epoch 64/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0025 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0024 Epoch 66/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0023 Epoch 67/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0022 Epoch 68/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0020 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0019 Epoch 70/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0017 Epoch 71/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0016 Epoch 72/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0016 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 76/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0015 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 78/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 79/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 80/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 82/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 84/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 85/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0013 Epoch 86/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0013 Epoch 87/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0013 Epoch 88/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 89/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0013 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 91/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0013 Epoch 92/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0013 Epoch 93/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 94/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 95/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 96/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0013 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 98/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0013 Epoch 99/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 1/1 [==============================] - 0s 438ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 13ms/step 1/1 [==============================] - 0s 14ms/step 1/1 [==============================] - 0s 13ms/step
Epoch 1/100 1/1 [==============================] - 2s 2s/step - loss: 0.4490 Epoch 2/100 1/1 [==============================] - 0s 4ms/step - loss: 0.4158 Epoch 3/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3837 Epoch 4/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3522 Epoch 5/100 1/1 [==============================] - 0s 4ms/step - loss: 0.3212 Epoch 6/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2905 Epoch 7/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2600 Epoch 8/100 1/1 [==============================] - 0s 4ms/step - loss: 0.2297 Epoch 9/100 1/1 [==============================] - 0s 3ms/step - loss: 0.1997 Epoch 10/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1700 Epoch 11/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1408 Epoch 12/100 1/1 [==============================] - 0s 4ms/step - loss: 0.1125 Epoch 13/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0855 Epoch 14/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0606 Epoch 15/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0386 Epoch 16/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0206 Epoch 17/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0080 Epoch 18/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0018 Epoch 19/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0029 Epoch 20/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0103 Epoch 21/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0210 Epoch 22/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0305 Epoch 23/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0352 Epoch 24/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0345 Epoch 25/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0295 Epoch 26/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0225 Epoch 27/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0152 Epoch 28/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0090 Epoch 29/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0047 Epoch 30/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0022 Epoch 31/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 32/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0020 Epoch 33/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0032 Epoch 34/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0047 Epoch 35/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0062 Epoch 36/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0074 Epoch 37/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 38/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0085 Epoch 39/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0082 Epoch 40/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0076 Epoch 41/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0067 Epoch 42/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0056 Epoch 43/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0044 Epoch 44/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0033 Epoch 45/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0024 Epoch 46/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0018 Epoch 47/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 48/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 49/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0016 Epoch 50/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0020 Epoch 51/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0023 Epoch 52/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0026 Epoch 53/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0028 Epoch 54/100 1/1 [==============================] - 0s 6ms/step - loss: 0.0028 Epoch 55/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0026 Epoch 56/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0024 Epoch 57/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0021 Epoch 58/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0017 Epoch 59/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 60/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 61/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0012 Epoch 62/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0012 Epoch 63/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 64/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0013 Epoch 65/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 66/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 67/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0015 Epoch 68/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0015 Epoch 69/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0014 Epoch 70/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0013 Epoch 71/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0013 Epoch 72/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0012 Epoch 73/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 74/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 75/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 76/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 77/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 78/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0011 Epoch 79/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0011 Epoch 80/100 1/1 [==============================] - 0s 3ms/step - loss: 0.0011 Epoch 81/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0011 Epoch 82/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0011 Epoch 83/100 1/1 [==============================] - 0s 4ms/step - loss: 0.0010 Epoch 84/100 1/1 [==============================] - 0s 5ms/step - loss: 0.0010 Epoch 85/100 1/1 [==============================] - 0s 4ms/step - loss: 9.7290e-04 Epoch 86/100 1/1 [==============================] - 0s 4ms/step - loss: 9.5115e-04 Epoch 87/100 1/1 [==============================] - 0s 5ms/step - loss: 9.3697e-04 Epoch 88/100 1/1 [==============================] - 0s 3ms/step - loss: 9.3026e-04 Epoch 89/100 1/1 [==============================] - 0s 4ms/step - loss: 9.2932e-04 Epoch 90/100 1/1 [==============================] - 0s 4ms/step - loss: 9.3154e-04 Epoch 91/100 1/1 [==============================] - 0s 5ms/step - loss: 9.3416e-04 Epoch 92/100 1/1 [==============================] - 0s 5ms/step - loss: 9.3492e-04 Epoch 93/100 1/1 [==============================] - 0s 5ms/step - loss: 9.3252e-04 Epoch 94/100 1/1 [==============================] - 0s 5ms/step - loss: 9.2672e-04 Epoch 95/100 1/1 [==============================] - 0s 5ms/step - loss: 9.1824e-04 Epoch 96/100 1/1 [==============================] - 0s 5ms/step - loss: 9.0841e-04 Epoch 97/100 1/1 [==============================] - 0s 4ms/step - loss: 8.9875e-04 Epoch 98/100 1/1 [==============================] - 0s 5ms/step - loss: 8.9057e-04 Epoch 99/100 1/1 [==============================] - 0s 5ms/step - loss: 8.8466e-04 Epoch 100/100 1/1 [==============================] - 0s 4ms/step - loss: 8.8120e-04 1/1 [==============================] - 0s 447ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 12ms/step 1/1 [==============================] - 0s 11ms/step 1/1 [==============================] - 0s 11ms/step
| year | Total Energy Consumption(10000 tons of SCE) | Proportion of Coal(%) | Proportion of Petroleum(%) | Proportion of Natural Gas(%) | Proportion of Primary Electricity and Other Energy(%) | Consumption of Coal(10000 tons) | Consumption of Coke(10000 tons) | Consumption of Crude Oil(10000 tons) | Consumption of Gasoline(10000 tons) | Consumption of Kerosene(10000 tons) | Consumption of Diesel Oil(10000 tons) | Consumption of Fuel Oil(10000 tons) | Consumption of Natural Gas(100 million cu.m) | Consumption of Electricity(100 million kwh) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2003 | 197083.0000 | 70.200000 | 20.100000 | 2.300000 | 7.400000 | 183760.24000 | 15926.470000 | 25180.720000 | 4118.520000 | 921.610000 | 8575.120000 | 4330.340000 | 339.080000 | 19031.600000 |
| 1 | 2004 | 230281.0000 | 70.200000 | 19.900000 | 2.300000 | 7.600000 | 212161.83000 | 18067.010000 | 29009.310000 | 4695.720000 | 1060.860000 | 10206.920000 | 4844.760000 | 396.720000 | 21971.370000 |
| 2 | 2005 | 261369.0000 | 72.400000 | 17.800000 | 2.400000 | 7.400000 | 243375.44000 | 25105.840000 | 30088.940000 | 4854.910000 | 1076.840000 | 10974.940000 | 4244.160000 | 466.080000 | 24940.320000 |
| 3 | 2006 | 286467.0000 | 72.400000 | 17.500000 | 2.700000 | 7.400000 | 270639.45000 | 28297.760000 | 32245.200000 | 5242.550000 | 1124.740000 | 11729.090000 | 4471.150000 | 573.320000 | 28587.970000 |
| 4 | 2007 | 311442.0000 | 72.500000 | 17.000000 | 3.000000 | 7.500000 | 290410.12000 | 31168.120000 | 34031.600000 | 5519.090000 | 1243.720000 | 12492.380000 | 4157.490000 | 705.230000 | 32711.810000 |
| 5 | 2008 | 320611.0000 | 71.500000 | 16.700000 | 3.400000 | 8.400000 | 300604.94000 | 32120.240000 | 35510.340000 | 6145.520000 | 1294.010000 | 13544.940000 | 3236.750000 | 812.940000 | 34541.350000 |
| 6 | 2009 | 336126.0000 | 71.600000 | 16.400000 | 3.500000 | 8.500000 | 325002.93000 | 36349.970000 | 38128.590000 | 6172.690000 | 1450.490000 | 13551.430000 | 2828.800000 | 895.200000 | 37032.140000 |
| 7 | 2010 | 360648.0000 | 69.200000 | 17.400000 | 4.000000 | 9.400000 | 349008.26000 | 38702.790000 | 42874.550000 | 6956.200000 | 1765.170000 | 14699.000000 | 3758.020000 | 1080.240000 | 41934.490000 |
| 8 | 2011 | 387043.0000 | 70.200000 | 16.800000 | 4.600000 | 8.400000 | 388961.10000 | 42063.280000 | 43965.840000 | 7595.950000 | 1816.720000 | 15635.100000 | 3662.800000 | 1341.070000 | 47000.880000 |
| 9 | 2012 | 402138.0000 | 68.500000 | 17.000000 | 4.800000 | 9.700000 | 411726.90000 | 44805.230000 | 46678.920000 | 8165.900000 | 1956.600000 | 16966.040000 | 3683.280000 | 1497.000000 | 49762.640000 |
| 10 | 2013 | 416913.0000 | 67.400000 | 17.100000 | 5.300000 | 10.200000 | 424425.94000 | 45851.870000 | 48652.150000 | 9366.350000 | 2164.070000 | 17150.650000 | 3953.970000 | 1705.370000 | 54203.410000 |
| 11 | 2014 | 428334.0000 | 65.800000 | 17.300000 | 5.600000 | 11.300000 | 413633.00000 | 46885.000000 | 51596.950000 | 9776.370000 | 2335.420000 | 17165.290000 | 4355.470000 | 1870.630000 | 57829.690000 |
| 12 | 2015 | 434113.0000 | 63.800000 | 18.400000 | 5.800000 | 12.000000 | 399834.00000 | 44059.000000 | 54788.280000 | 11368.460000 | 2663.710000 | 17360.310000 | 4662.010000 | 1931.750000 | 58019.980000 |
| 13 | 2016 | 441492.0000 | 62.200000 | 18.700000 | 6.100000 | 13.000000 | 388820.00000 | 45462.000000 | 57125.930000 | 11866.040000 | 2970.710000 | 16839.040000 | 4631.040000 | 2078.060000 | 61205.090000 |
| 14 | 2017 | 455827.0000 | 60.600000 | 18.900000 | 6.900000 | 13.600000 | 391403.00000 | 43743.000000 | 59402.170000 | 12296.270000 | 3326.360000 | 16916.540000 | 4887.300000 | 2393.690000 | 65913.970000 |
| 15 | 2018 | 471925.0000 | 59.000000 | 18.900000 | 7.600000 | 14.500000 | 397452.00000 | 43717.000000 | 63004.330000 | 13055.300000 | 3653.510000 | 16409.560000 | 4536.070000 | 2817.090000 | 71508.200000 |
| 16 | 2019 | 487488.0000 | 57.700000 | 19.000000 | 8.000000 | 15.300000 | 401915.00000 | 46426.000000 | 67268.270000 | 13627.970000 | 3950.230000 | 14917.950000 | 4690.340000 | 3059.680000 | 74866.120000 |
| 17 | 2020 | 498314.0000 | 56.900000 | 18.800000 | 8.400000 | 15.900000 | 404860.00000 | 48310.000000 | 69477.140000 | 12767.160000 | 3352.100000 | 14282.700000 | 5364.600000 | 3339.890000 | 77620.170000 |
| 18 | 2021 | 524000.0000 | 56.000000 | 18.500000 | 8.900000 | 16.600000 | 409987.12500 | 47249.488281 | 73206.304688 | 14802.031250 | 4382.992188 | 16466.960938 | 4521.073730 | 3742.249756 | 81274.703125 |
| 19 | 2022 | 541000.0000 | 56.200000 | 18.124666 | 9.945584 | 18.330135 | 412244.09375 | 47433.878906 | 76895.078125 | 15292.958984 | 4678.936035 | 16311.519531 | 4528.297852 | 4254.971680 | 85783.210938 |
| 20 | 2023 | 534874.6250 | 53.667168 | 17.976780 | 10.689278 | 19.428606 | 414670.65625 | 47985.304688 | 81011.765625 | 15839.913086 | 4944.760742 | 16168.312500 | 4498.509277 | 4804.278320 | 90022.226562 |
| 21 | 2024 | 547432.0000 | 53.061035 | 17.849726 | 11.412879 | 20.565023 | 416650.18750 | 48621.925781 | 85112.640625 | 16345.994141 | 5192.088867 | 16108.673828 | 4489.799316 | 5354.038086 | 93704.507812 |
| 22 | 2025 | 558753.8125 | 52.537197 | 17.819319 | 12.261145 | 21.793034 | 418353.25000 | 48882.558594 | 88971.835938 | 16858.433594 | 5432.677734 | 16262.715820 | 4463.012695 | 6012.355957 | 97516.695312 |
| 23 | 2026 | 569917.7500 | 52.032959 | 17.845015 | 13.257030 | 23.203493 | 419957.25000 | 48883.785156 | 93389.843750 | 17774.503906 | 5986.672363 | 16514.144531 | 4379.600098 | 6782.358398 | 101655.750000 |
| 24 | 2027 | 577079.3750 | 51.581459 | 17.897102 | 14.378264 | 24.767618 | 421070.65625 | 49099.863281 | 97851.554688 | 18300.417969 | 6290.188477 | 16495.296875 | 4367.132324 | 7619.170410 | 105853.789062 |
| 25 | 2028 | 582170.3125 | 51.045490 | 17.934679 | 15.406851 | 26.110659 | 422082.78125 | 49327.128906 | 102395.484375 | 18851.275391 | 6592.424316 | 16505.386719 | 4351.123047 | 8477.360352 | 109846.609375 |
| 26 | 2029 | 590807.4375 | 50.797585 | 17.973047 | 16.475143 | 27.463623 | 422931.25000 | 49500.972656 | 106884.000000 | 19407.974609 | 6902.262695 | 16541.410156 | 4336.054199 | 9342.809570 | 113704.679688 |
| 27 | 2030 | 598347.6250 | 50.580982 | 17.995325 | 17.596319 | 28.809925 | 423651.90625 | 49601.191406 | 111330.640625 | 19977.380859 | 7224.102539 | 16591.585938 | 4320.798828 | 10213.050781 | 117583.570312 |
| 28 | 2031 | 604975.3750 | 50.393036 | 18.004433 | 18.722374 | 30.114101 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 29 | 2032 | 610608.6875 | 50.237183 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
from prophet import Prophet
# Define the list of columns to forecast
cols = ["Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)",
"Total Energy Consumption, Manufacturing(10000 tons of SCE)",
"Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)",
"Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)"]
Prophet_Results = []
for col in cols:
# Load data
target_column = col
df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Consumption by Sector.xls")
# Preprocessing: Set the index, select relevant columns, and rename them
df.set_index("Databaseï¼Annual", inplace=True)
# Prepare the data
new_df = df.T[["Indicators", target_column]].reset_index(drop=True)
new_df["Indicators"] =new_df["Indicators"].astype("int")
new_df["date"] = pd.to_datetime(new_df["Indicators"].astype(str) + "-12-31")
new_df = new_df.rename(columns={"date": "ds", target_column: "y"}).sort_values(by="ds")
new_df.dropna(inplace=True)
# Create and fit the Prophet model
model = Prophet()
model.fit(new_df)
# Make future dataframe for forecasting
future = model.make_future_dataframe(periods=10, freq="Y")
# Forecast the next 10 data points
forecast = model.predict(future)
# Append the forecasted values to the original data
extended_t = future["ds"]
extended_y = np.concatenate([new_df["y"].values, forecast["yhat"][-10:]])
Result = pd.DataFrame({"Year": extended_t, target_column: extended_y})
Result["Year"] = Result["Year"].dt.year
Prophet_Results.append(Result)
# Plot original data and forecasted values
plt.figure(figsize=(10, 6))
plt.plot(new_df["ds"], new_df["y"], color="dodgerblue", linewidth=2, label="Original Data")
plt.plot(forecast["ds"][-10:], forecast["yhat"][-10:], linestyle="dashed", color="darkorange", label="Forecasted Values")
plt.fill_between(forecast["ds"][-10:], forecast["yhat_lower"][-10:], forecast["yhat_upper"][-10:], color="darkorange", alpha=0.1)
plt.xlabel("Year")
plt.ylabel(target_column)
plt.title("Prophet Forecast")
plt.grid(True)
plt.legend()
plt.show()
Final_Prophet_Results1 = pd.concat(Prophet_Results, axis=1)
Final_Prophet_Results1.columns.values[0] = "year"
# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_Prophet_Results1.columns if col == "Year"][1:]
Final_Prophet_Results1.drop(columns=cols_to_drop, inplace=True)
Final_Prophet_Results1
23:55:13 - cmdstanpy - INFO - Chain [1] start processing 23:55:13 - cmdstanpy - INFO - Chain [1] done processing
23:55:13 - cmdstanpy - INFO - Chain [1] start processing 23:55:13 - cmdstanpy - INFO - Chain [1] done processing
23:55:13 - cmdstanpy - INFO - Chain [1] start processing 23:55:13 - cmdstanpy - INFO - Chain [1] done processing
23:55:14 - cmdstanpy - INFO - Chain [1] start processing 23:55:14 - cmdstanpy - INFO - Chain [1] done processing
| year | Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE) | Total Energy Consumption, Manufacturing(10000 tons of SCE) | Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE) | Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE) | |
|---|---|---|---|---|---|
| 0 | 2003 | 5683.210000 | 111222.870000 | 1098.680000 | 4723.400000 |
| 1 | 2004 | 6391.860000 | 136407.850000 | 1316.210000 | 5498.790000 |
| 2 | 2005 | 6860.460000 | 158234.920000 | 1517.970000 | 5916.630000 |
| 3 | 2006 | 7153.520000 | 174920.090000 | 1886.510000 | 6358.180000 |
| 4 | 2007 | 7068.450000 | 193133.070000 | 2096.360000 | 6731.960000 |
| 5 | 2008 | 6872.630000 | 198406.180000 | 2219.950000 | 6884.910000 |
| 6 | 2009 | 6978.210000 | 206555.600000 | 2228.680000 | 7303.220000 |
| 7 | 2010 | 7266.500000 | 217328.870000 | 2547.390000 | 7847.100000 |
| 8 | 2011 | 7675.230000 | 229090.990000 | 2650.410000 | 9147.500000 |
| 9 | 2012 | 7803.570000 | 234538.810000 | 2689.440000 | 10012.330000 |
| 10 | 2013 | 8054.800000 | 239053.400000 | 2801.590000 | 10598.160000 |
| 11 | 2014 | 8020.000000 | 248976.000000 | 2968.000000 | 10864.000000 |
| 12 | 2015 | 8271.000000 | 248264.000000 | 3149.000000 | 11447.000000 |
| 13 | 2016 | 8585.000000 | 247658.000000 | 3377.000000 | 12042.000000 |
| 14 | 2017 | 8945.000000 | 252462.000000 | 3662.000000 | 12456.000000 |
| 15 | 2018 | 8781.000000 | 258604.000000 | 4628.000000 | 12994.000000 |
| 16 | 2019 | 9018.000000 | 268426.000000 | 5028.000000 | 13624.000000 |
| 17 | 2020 | 9263.000000 | 279651.000000 | 5120.000000 | 13171.000000 |
| 18 | 2021 | 9483.672433 | 279005.864547 | 4824.790275 | 14490.469982 |
| 19 | 2022 | 9640.351806 | 284063.010650 | 5080.972749 | 15080.286198 |
| 20 | 2023 | 9723.883430 | 288983.878013 | 5367.006228 | 15694.228230 |
| 21 | 2024 | 9980.283956 | 294467.337543 | 5453.784355 | 16119.260677 |
| 22 | 2025 | 10210.157250 | 299659.728062 | 5680.056562 | 16684.926141 |
| 23 | 2026 | 10366.836623 | 304716.874166 | 5936.239036 | 17274.742357 |
| 24 | 2027 | 10450.368247 | 309637.741529 | 6222.272515 | 17888.684389 |
| 25 | 2028 | 10706.768773 | 315121.201059 | 6309.050642 | 18313.716837 |
| 26 | 2029 | 10936.642067 | 320313.591578 | 6535.322849 | 18879.382300 |
| 27 | 2030 | 11093.321440 | 325370.737682 | 6791.505323 | 19469.198517 |
from prophet import Prophet
# Define the list of columns to forecast
cols = ["Total Energy Consumption(10000 tons of SCE)",
"Proportion of Coal(%)",
"Proportion of Petroleum(%)",
"Proportion of Natural Gas(%)",
"Proportion of Primary Electricity and Other Energy(%)",
"Consumption of Coal(10000 tons)",
"Consumption of Coke(10000 tons)",
"Consumption of Crude Oil(10000 tons)",
"Consumption of Gasoline(10000 tons)",
"Consumption of Kerosene(10000 tons)",
"Consumption of Diesel Oil(10000 tons)",
"Consumption of Fuel Oil(10000 tons)",
"Consumption of Natural Gas(100 million cu.m)",
"Consumption of Electricity(100 million kwh)"]
Prophet_Results = []
for col in cols:
# Load data
target_column = col
df = pd.read_excel("D:\Jupyter Directory\Dissertation\Dissertation Datasets\Ones I will use\Annual Total Energy Consumption.xls")
# Preprocessing: Set the index, select relevant columns, and rename them
df.set_index("Databaseï¼Annual", inplace=True)
# Prepare the data
new_df = df.T[["Indicators", target_column]].reset_index(drop=True)
new_df["Indicators"] =new_df["Indicators"].astype("int")
new_df["date"] = pd.to_datetime(new_df["Indicators"].astype(str) + "-12-31")
new_df = new_df.rename(columns={"date": "ds", target_column: "y"}).sort_values(by="ds")
new_df.dropna(inplace=True)
# Create and fit the Prophet model
model = Prophet()
model.fit(new_df)
# Make future dataframe for forecasting
future = model.make_future_dataframe(periods=10, freq="Y")
# Forecast the next 10 data points
forecast = model.predict(future)
# Append the forecasted values to the original data
extended_t = future["ds"]
extended_y = np.concatenate([new_df["y"].values, forecast["yhat"][-10:]])
Result = pd.DataFrame({"Year": extended_t, target_column: extended_y})
Result["Year"] = Result["Year"].dt.year
Prophet_Results.append(Result)
# Plot original data and forecasted values
plt.figure(figsize=(10, 6))
plt.plot(new_df["ds"], new_df["y"], color="dodgerblue", linewidth=2, label="Original Data")
plt.plot(forecast["ds"][-10:], forecast["yhat"][-10:], linestyle="dashed", color="darkorange", label="Forecasted Values")
plt.fill_between(forecast["ds"][-10:], forecast["yhat_lower"][-10:], forecast["yhat_upper"][-10:], color="darkorange", alpha=0.1)
plt.xlabel("Year")
plt.ylabel(target_column)
plt.title("Prophet Forecast")
plt.grid(True)
plt.legend()
plt.show()
Final_Prophet_Results2= pd.concat(Prophet_Results, axis=1)
Final_Prophet_Results2.columns.values[0] = "year"
# Assuming 'df' is your DataFrame
cols_to_drop = [col for col in Final_Prophet_Results2.columns if col == "Year"][1:]
Final_Prophet_Results2.drop(columns=cols_to_drop, inplace=True)
Final_Prophet_Results2
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| year | Total Energy Consumption(10000 tons of SCE) | Proportion of Coal(%) | Proportion of Petroleum(%) | Proportion of Natural Gas(%) | Proportion of Primary Electricity and Other Energy(%) | Consumption of Coal(10000 tons) | Consumption of Coke(10000 tons) | Consumption of Crude Oil(10000 tons) | Consumption of Gasoline(10000 tons) | Consumption of Kerosene(10000 tons) | Consumption of Diesel Oil(10000 tons) | Consumption of Fuel Oil(10000 tons) | Consumption of Natural Gas(100 million cu.m) | Consumption of Electricity(100 million kwh) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2003 | 197083.000000 | 70.200000 | 20.100000 | 2.300000 | 7.400000 | 183760.240000 | 15926.470000 | 25180.720000 | 4118.520000 | 921.610000 | 8575.120000 | 4330.340000 | 339.080000 | 19031.600000 |
| 1 | 2004 | 230281.000000 | 70.200000 | 19.900000 | 2.300000 | 7.600000 | 212161.830000 | 18067.010000 | 29009.310000 | 4695.720000 | 1060.860000 | 10206.920000 | 4844.760000 | 396.720000 | 21971.370000 |
| 2 | 2005 | 261369.000000 | 72.400000 | 17.800000 | 2.400000 | 7.400000 | 243375.440000 | 25105.840000 | 30088.940000 | 4854.910000 | 1076.840000 | 10974.940000 | 4244.160000 | 466.080000 | 24940.320000 |
| 3 | 2006 | 286467.000000 | 72.400000 | 17.500000 | 2.700000 | 7.400000 | 270639.450000 | 28297.760000 | 32245.200000 | 5242.550000 | 1124.740000 | 11729.090000 | 4471.150000 | 573.320000 | 28587.970000 |
| 4 | 2007 | 311442.000000 | 72.500000 | 17.000000 | 3.000000 | 7.500000 | 290410.120000 | 31168.120000 | 34031.600000 | 5519.090000 | 1243.720000 | 12492.380000 | 4157.490000 | 705.230000 | 32711.810000 |
| 5 | 2008 | 320611.000000 | 71.500000 | 16.700000 | 3.400000 | 8.400000 | 300604.940000 | 32120.240000 | 35510.340000 | 6145.520000 | 1294.010000 | 13544.940000 | 3236.750000 | 812.940000 | 34541.350000 |
| 6 | 2009 | 336126.000000 | 71.600000 | 16.400000 | 3.500000 | 8.500000 | 325002.930000 | 36349.970000 | 38128.590000 | 6172.690000 | 1450.490000 | 13551.430000 | 2828.800000 | 895.200000 | 37032.140000 |
| 7 | 2010 | 360648.000000 | 69.200000 | 17.400000 | 4.000000 | 9.400000 | 349008.260000 | 38702.790000 | 42874.550000 | 6956.200000 | 1765.170000 | 14699.000000 | 3758.020000 | 1080.240000 | 41934.490000 |
| 8 | 2011 | 387043.000000 | 70.200000 | 16.800000 | 4.600000 | 8.400000 | 388961.100000 | 42063.280000 | 43965.840000 | 7595.950000 | 1816.720000 | 15635.100000 | 3662.800000 | 1341.070000 | 47000.880000 |
| 9 | 2012 | 402138.000000 | 68.500000 | 17.000000 | 4.800000 | 9.700000 | 411726.900000 | 44805.230000 | 46678.920000 | 8165.900000 | 1956.600000 | 16966.040000 | 3683.280000 | 1497.000000 | 49762.640000 |
| 10 | 2013 | 416913.000000 | 67.400000 | 17.100000 | 5.300000 | 10.200000 | 424425.940000 | 45851.870000 | 48652.150000 | 9366.350000 | 2164.070000 | 17150.650000 | 3953.970000 | 1705.370000 | 54203.410000 |
| 11 | 2014 | 428334.000000 | 65.800000 | 17.300000 | 5.600000 | 11.300000 | 413633.000000 | 46885.000000 | 51596.950000 | 9776.370000 | 2335.420000 | 17165.290000 | 4355.470000 | 1870.630000 | 57829.690000 |
| 12 | 2015 | 434113.000000 | 63.800000 | 18.400000 | 5.800000 | 12.000000 | 399834.000000 | 44059.000000 | 54788.280000 | 11368.460000 | 2663.710000 | 17360.310000 | 4662.010000 | 1931.750000 | 58019.980000 |
| 13 | 2016 | 441492.000000 | 62.200000 | 18.700000 | 6.100000 | 13.000000 | 388820.000000 | 45462.000000 | 57125.930000 | 11866.040000 | 2970.710000 | 16839.040000 | 4631.040000 | 2078.060000 | 61205.090000 |
| 14 | 2017 | 455827.000000 | 60.600000 | 18.900000 | 6.900000 | 13.600000 | 391403.000000 | 43743.000000 | 59402.170000 | 12296.270000 | 3326.360000 | 16916.540000 | 4887.300000 | 2393.690000 | 65913.970000 |
| 15 | 2018 | 471925.000000 | 59.000000 | 18.900000 | 7.600000 | 14.500000 | 397452.000000 | 43717.000000 | 63004.330000 | 13055.300000 | 3653.510000 | 16409.560000 | 4536.070000 | 2817.090000 | 71508.200000 |
| 16 | 2019 | 487488.000000 | 57.700000 | 19.000000 | 8.000000 | 15.300000 | 401915.000000 | 46426.000000 | 67268.270000 | 13627.970000 | 3950.230000 | 14917.950000 | 4690.340000 | 3059.680000 | 74866.120000 |
| 17 | 2020 | 498314.000000 | 56.900000 | 18.800000 | 8.400000 | 15.900000 | 404860.000000 | 48310.000000 | 69477.140000 | 12767.160000 | 3352.100000 | 14282.700000 | 5364.600000 | 3339.890000 | 77620.170000 |
| 18 | 2021 | 524000.000000 | 56.000000 | 18.500000 | 8.900000 | 16.600000 | 465297.962695 | 54254.818304 | 72172.246077 | 14196.594002 | 3797.397907 | 18299.402048 | 4669.051809 | 3153.539418 | 81022.389067 |
| 19 | 2022 | 541000.000000 | 56.200000 | 19.164792 | 9.435511 | 17.399416 | 478402.023070 | 55948.450110 | 75255.216217 | 14898.577992 | 4032.950992 | 18641.232188 | 4736.370677 | 3339.750626 | 84910.813950 |
| 20 | 2023 | 555943.601985 | 54.275223 | 19.636072 | 10.000462 | 18.113629 | 488681.510792 | 57309.920985 | 78510.418625 | 15659.831879 | 4271.736685 | 18854.553854 | 4827.712475 | 3555.300952 | 88684.399181 |
| 21 | 2024 | 568674.817929 | 53.129654 | 19.802041 | 10.364465 | 19.053135 | 499724.231787 | 59149.022362 | 81294.569487 | 15974.065764 | 4284.845321 | 19422.761755 | 4816.734905 | 3696.375629 | 91299.311283 |
| 22 | 2025 | 588364.678969 | 52.615802 | 19.735367 | 10.893872 | 19.763673 | 515652.256433 | 61174.858907 | 84204.175306 | 16616.670506 | 4517.098724 | 19893.168770 | 4860.004103 | 3853.232038 | 95301.711116 |
| 23 | 2026 | 606527.487063 | 51.736206 | 19.937720 | 11.441089 | 20.476083 | 528756.316808 | 62868.490713 | 87287.145446 | 17318.654497 | 4752.651808 | 20234.998911 | 4927.322971 | 4039.443246 | 99190.135999 |
| 24 | 2027 | 623160.930674 | 50.491853 | 20.408999 | 12.006039 | 21.190296 | 539035.804530 | 64229.961588 | 90542.347854 | 18079.908384 | 4991.437501 | 20448.320577 | 5018.664769 | 4254.993572 | 102963.721230 |
| 25 | 2028 | 635892.146619 | 49.346283 | 20.574969 | 12.370043 | 22.129802 | 550078.525525 | 66069.062965 | 93326.498716 | 18394.142269 | 5004.546137 | 21016.528478 | 5007.687199 | 4396.068249 | 105578.633332 |
| 26 | 2029 | 655582.007658 | 48.832432 | 20.508295 | 12.899450 | 22.840340 | 566006.550171 | 68094.899510 | 96236.104535 | 19036.747011 | 5236.799540 | 21486.935493 | 5050.956397 | 4552.924658 | 109581.033165 |
| 27 | 2030 | 673744.815753 | 47.952836 | 20.710648 | 13.446667 | 23.552750 | 579110.610547 | 69788.531315 | 99319.074676 | 19738.731002 | 5472.352624 | 21828.765633 | 5118.275265 | 4739.135866 | 113469.458048 |
| 28 | 2031 | 690378.259364 | 46.708482 | 21.181927 | 14.011617 | 24.266963 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 29 | 2032 | 703109.475309 | 45.562913 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# Calculate the average for each corresponding cell in the three dataframes
Average1 = (Final_Arima_Results1 + Final_LSTM_Results1 + Final_Prophet_Results1) / 3
print("Average1 DataFrame:")
Average1
Average1 DataFrame:
| year | Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE) | Total Energy Consumption, Manufacturing(10000 tons of SCE) | Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE) | Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE) | |
|---|---|---|---|---|---|
| 0 | 2003.0 | 5683.210000 | 111222.870000 | 1098.680000 | 4723.400000 |
| 1 | 2004.0 | 6391.860000 | 136407.850000 | 1316.210000 | 5498.790000 |
| 2 | 2005.0 | 6860.460000 | 158234.920000 | 1517.970000 | 5916.630000 |
| 3 | 2006.0 | 7153.520000 | 174920.090000 | 1886.510000 | 6358.180000 |
| 4 | 2007.0 | 7068.450000 | 193133.070000 | 2096.360000 | 6731.960000 |
| 5 | 2008.0 | 6872.630000 | 198406.180000 | 2219.950000 | 6884.910000 |
| 6 | 2009.0 | 6978.210000 | 206555.600000 | 2228.680000 | 7303.220000 |
| 7 | 2010.0 | 7266.500000 | 217328.870000 | 2547.390000 | 7847.100000 |
| 8 | 2011.0 | 7675.230000 | 229090.990000 | 2650.410000 | 9147.500000 |
| 9 | 2012.0 | 7803.570000 | 234538.810000 | 2689.440000 | 10012.330000 |
| 10 | 2013.0 | 8054.800000 | 239053.400000 | 2801.590000 | 10598.160000 |
| 11 | 2014.0 | 8020.000000 | 248976.000000 | 2968.000000 | 10864.000000 |
| 12 | 2015.0 | 8271.000000 | 248264.000000 | 3149.000000 | 11447.000000 |
| 13 | 2016.0 | 8585.000000 | 247658.000000 | 3377.000000 | 12042.000000 |
| 14 | 2017.0 | 8945.000000 | 252462.000000 | 3662.000000 | 12456.000000 |
| 15 | 2018.0 | 8781.000000 | 258604.000000 | 4628.000000 | 12994.000000 |
| 16 | 2019.0 | 9018.000000 | 268426.000000 | 5028.000000 | 13624.000000 |
| 17 | 2020.0 | 9263.000000 | 279651.000000 | 5120.000000 | 13171.000000 |
| 18 | 2021.0 | 9481.567539 | 278522.038507 | 5361.747765 | 14167.904926 |
| 19 | 2022.0 | 9665.232297 | 282934.292368 | 5832.958457 | 14660.843232 |
| 20 | 2023.0 | 9803.879329 | 287043.327606 | 6389.149432 | 15164.928945 |
| 21 | 2024.0 | 10036.021723 | 291007.693016 | 6854.775635 | 15596.842323 |
| 22 | 2025.0 | 10253.871320 | 294303.054059 | 7464.092410 | 16056.412995 |
| 23 | 2026.0 | 10441.046755 | 296882.722568 | 8256.227256 | 16601.502994 |
| 24 | 2027.0 | 10601.538323 | 299953.909243 | 9107.472593 | 17087.584801 |
| 25 | 2028.0 | 10821.000574 | 302986.243368 | 9902.722982 | 17509.969207 |
| 26 | 2029.0 | 11039.507615 | 305710.560568 | 10697.532921 | 17976.630375 |
| 27 | 2030.0 | 11230.145226 | 308211.842699 | 11446.398236 | 18450.014970 |
import seaborn as sns
import matplotlib.pyplot as plt
# Convert 'Year' column to string
# Remove the last two characters from the 'year' column
Average1["year"] = Average1["year"].astype(str).str[:-2]
# Set seaborn style
sns.set(style="whitegrid")
# Iterate through each column and create separate plots
for col in Average1.columns[1:]:
# Set the plot size
plt.figure(figsize=(10, 6))
ax = sns.lineplot(data=Average1, x="year", y=col, marker="o")
ax.set_xlabel("Year")
ax.set_ylabel("Values")
ax.set_title(f"Average Plot for {col}")
ax.yaxis.grid(True) # Show only horizontal gridlines
ax.xaxis.grid(False) # Turn off vertical gridlines
# Rotate x-axis tick labels by 45 degrees
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
# Customize plot appearance
sns.despine() # Remove spines (axis lines)
ax.tick_params(axis="both", which="both", length=0) # Remove tick marks
ax.set_facecolor("#f0f0f0") # Set plot background color
ax.grid(color="white", linestyle="-", linewidth=0.5) # Adjust gridlines
plt.tight_layout() # Improve spacing between plots
plt.show()
# Calculate the average for each corresponding cell
Average2 = (Final_Arima_Results2 + Final_LSTM_Results2 + Final_Prophet_Results2) / 3
print("Average2 Dataframe:")
Average2
Average2 Dataframe:
| year | Total Energy Consumption(10000 tons of SCE) | Proportion of Coal(%) | Proportion of Petroleum(%) | Proportion of Natural Gas(%) | Proportion of Primary Electricity and Other Energy(%) | Consumption of Coal(10000 tons) | Consumption of Coke(10000 tons) | Consumption of Crude Oil(10000 tons) | Consumption of Gasoline(10000 tons) | Consumption of Kerosene(10000 tons) | Consumption of Diesel Oil(10000 tons) | Consumption of Fuel Oil(10000 tons) | Consumption of Natural Gas(100 million cu.m) | Consumption of Electricity(100 million kwh) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2003.0 | 197083.000000 | 70.200000 | 20.100000 | 2.300000 | 7.400000 | 183760.240000 | 15926.470000 | 25180.720000 | 4118.520000 | 921.610000 | 8575.120000 | 4330.340000 | 339.080000 | 19031.600000 |
| 1 | 2004.0 | 230281.000000 | 70.200000 | 19.900000 | 2.300000 | 7.600000 | 212161.830000 | 18067.010000 | 29009.310000 | 4695.720000 | 1060.860000 | 10206.920000 | 4844.760000 | 396.720000 | 21971.370000 |
| 2 | 2005.0 | 261369.000000 | 72.400000 | 17.800000 | 2.400000 | 7.400000 | 243375.440000 | 25105.840000 | 30088.940000 | 4854.910000 | 1076.840000 | 10974.940000 | 4244.160000 | 466.080000 | 24940.320000 |
| 3 | 2006.0 | 286467.000000 | 72.400000 | 17.500000 | 2.700000 | 7.400000 | 270639.450000 | 28297.760000 | 32245.200000 | 5242.550000 | 1124.740000 | 11729.090000 | 4471.150000 | 573.320000 | 28587.970000 |
| 4 | 2007.0 | 311442.000000 | 72.500000 | 17.000000 | 3.000000 | 7.500000 | 290410.120000 | 31168.120000 | 34031.600000 | 5519.090000 | 1243.720000 | 12492.380000 | 4157.490000 | 705.230000 | 32711.810000 |
| 5 | 2008.0 | 320611.000000 | 71.500000 | 16.700000 | 3.400000 | 8.400000 | 300604.940000 | 32120.240000 | 35510.340000 | 6145.520000 | 1294.010000 | 13544.940000 | 3236.750000 | 812.940000 | 34541.350000 |
| 6 | 2009.0 | 336126.000000 | 71.600000 | 16.400000 | 3.500000 | 8.500000 | 325002.930000 | 36349.970000 | 38128.590000 | 6172.690000 | 1450.490000 | 13551.430000 | 2828.800000 | 895.200000 | 37032.140000 |
| 7 | 2010.0 | 360648.000000 | 69.200000 | 17.400000 | 4.000000 | 9.400000 | 349008.260000 | 38702.790000 | 42874.550000 | 6956.200000 | 1765.170000 | 14699.000000 | 3758.020000 | 1080.240000 | 41934.490000 |
| 8 | 2011.0 | 387043.000000 | 70.200000 | 16.800000 | 4.600000 | 8.400000 | 388961.100000 | 42063.280000 | 43965.840000 | 7595.950000 | 1816.720000 | 15635.100000 | 3662.800000 | 1341.070000 | 47000.880000 |
| 9 | 2012.0 | 402138.000000 | 68.500000 | 17.000000 | 4.800000 | 9.700000 | 411726.900000 | 44805.230000 | 46678.920000 | 8165.900000 | 1956.600000 | 16966.040000 | 3683.280000 | 1497.000000 | 49762.640000 |
| 10 | 2013.0 | 416913.000000 | 67.400000 | 17.100000 | 5.300000 | 10.200000 | 424425.940000 | 45851.870000 | 48652.150000 | 9366.350000 | 2164.070000 | 17150.650000 | 3953.970000 | 1705.370000 | 54203.410000 |
| 11 | 2014.0 | 428334.000000 | 65.800000 | 17.300000 | 5.600000 | 11.300000 | 413633.000000 | 46885.000000 | 51596.950000 | 9776.370000 | 2335.420000 | 17165.290000 | 4355.470000 | 1870.630000 | 57829.690000 |
| 12 | 2015.0 | 434113.000000 | 63.800000 | 18.400000 | 5.800000 | 12.000000 | 399834.000000 | 44059.000000 | 54788.280000 | 11368.460000 | 2663.710000 | 17360.310000 | 4662.010000 | 1931.750000 | 58019.980000 |
| 13 | 2016.0 | 441492.000000 | 62.200000 | 18.700000 | 6.100000 | 13.000000 | 388820.000000 | 45462.000000 | 57125.930000 | 11866.040000 | 2970.710000 | 16839.040000 | 4631.040000 | 2078.060000 | 61205.090000 |
| 14 | 2017.0 | 455827.000000 | 60.600000 | 18.900000 | 6.900000 | 13.600000 | 391403.000000 | 43743.000000 | 59402.170000 | 12296.270000 | 3326.360000 | 16916.540000 | 4887.300000 | 2393.690000 | 65913.970000 |
| 15 | 2018.0 | 471925.000000 | 59.000000 | 18.900000 | 7.600000 | 14.500000 | 397452.000000 | 43717.000000 | 63004.330000 | 13055.300000 | 3653.510000 | 16409.560000 | 4536.070000 | 2817.090000 | 71508.200000 |
| 16 | 2019.0 | 487488.000000 | 57.700000 | 19.000000 | 8.000000 | 15.300000 | 401915.000000 | 46426.000000 | 67268.270000 | 13627.970000 | 3950.230000 | 14917.950000 | 4690.340000 | 3059.680000 | 74866.120000 |
| 17 | 2020.0 | 498314.000000 | 56.900000 | 18.800000 | 8.400000 | 15.900000 | 404860.000000 | 48310.000000 | 69477.140000 | 12767.160000 | 3352.100000 | 14282.700000 | 5364.600000 | 3339.890000 | 77620.170000 |
| 18 | 2021.0 | 524000.000000 | 56.000000 | 18.500000 | 8.900000 | 16.600000 | 427258.939324 | 50677.440669 | 72487.120843 | 14091.509594 | 3891.820032 | 16137.937662 | 4782.566014 | 3505.296391 | 81121.216221 |
| 19 | 2022.0 | 541000.000000 | 56.200000 | 18.414099 | 9.549254 | 17.613554 | 432846.562047 | 52239.139613 | 75612.925957 | 14658.728012 | 4116.642342 | 15988.317240 | 4753.169750 | 3831.677435 | 85068.989276 |
| 20 | 2023.0 | 549132.750791 | 54.468516 | 18.339863 | 10.107691 | 18.388152 | 437483.357697 | 53503.731891 | 78938.779848 | 15264.378518 | 4332.502476 | 15799.938785 | 4731.049624 | 4180.033091 | 88888.651718 |
| 21 | 2024.0 | 562698.460287 | 53.639002 | 18.205704 | 10.592448 | 19.250497 | 442168.995153 | 54842.229741 | 82102.345724 | 15707.398007 | 4466.971396 | 15757.711861 | 4690.972069 | 4503.714572 | 92136.511659 |
| 22 | 2025.0 | 578001.716143 | 53.047491 | 18.087898 | 11.173894 | 20.067050 | 448342.320507 | 56075.767405 | 85227.170022 | 16261.993916 | 4672.242153 | 15754.111530 | 4670.122113 | 4868.842665 | 95890.169594 |
| 23 | 2026.0 | 592701.142570 | 52.340599 | 18.154348 | 11.810483 | 20.945044 | 453499.559007 | 57096.678915 | 88596.053262 | 16970.926527 | 4983.081390 | 15740.114481 | 4644.049997 | 5280.983881 | 99714.791608 |
| 24 | 2027.0 | 605536.587985 | 51.529700 | 18.394435 | 12.494767 | 21.874860 | 457515.878755 | 58072.807337 | 92036.914965 | 17569.563686 | 5211.505326 | 15593.189151 | 4654.064395 | 5725.174661 | 103520.795195 |
| 25 | 2028.0 | 616368.271189 | 50.723574 | 18.588178 | 13.081186 | 22.806079 | 461722.038601 | 59209.783212 | 95348.165736 | 18027.508632 | 5364.276818 | 15574.205066 | 4632.274460 | 6151.666200 | 106872.168157 |
| 26 | 2029.0 | 630696.861031 | 50.224041 | 18.739015 | 13.735974 | 23.664284 | 467475.611177 | 60390.426429 | 98682.763473 | 18596.857795 | 5592.630745 | 15531.265216 | 4631.617737 | 6585.838076 | 110641.120363 |
| 27 | 2030.0 | 644148.431186 | 49.613027 | 18.976984 | 14.414327 | 24.520892 | 472222.517695 | 61435.509328 | 102061.190983 | 19190.235718 | 5826.085054 | 15450.183857 | 4641.067163 | 7031.392216 | 114379.021022 |
| 28 | 2031.0 | 656784.901236 | 48.889980 | 19.271968 | 15.100218 | 25.364059 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 29 | 2032.0 | 667788.641682 | 48.210558 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
import seaborn as sns
import matplotlib.pyplot as plt
# Convert 'Year' column to string
# Remove the last two characters from the 'year' column
Average2["year"] = Average2["year"].astype(str).str[:-2]
# Set seaborn style
sns.set(style="whitegrid")
# Iterate through each column and create separate plots
for col in Average2.columns[1:]:
# Set the plot size
plt.figure(figsize=(10, 6))
ax = sns.lineplot(data=Average2, x="year", y=col, marker="o")
ax.set_xlabel("Year")
ax.set_ylabel("Values")
ax.set_title(f"Average Plot for {col}")
ax.yaxis.grid(True) # Show only horizontal gridlines
ax.xaxis.grid(False) # Turn off vertical gridlines
# Rotate x-axis tick labels by 45 degrees
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
# Customize plot appearance
sns.despine() # Remove spines (axis lines)
ax.tick_params(axis="both", which="both", length=0) # Remove tick marks
ax.set_facecolor("#f0f0f0") # Set plot background color
ax.grid(color="white", linestyle="-", linewidth=0.5) # Adjust gridlines
plt.tight_layout() # Improve spacing between plots
plt.show()
from sklearn.metrics import mean_squared_error
import numpy as np
# Assuming you have calculated your Average2 DataFrame
# And you have individual forecast DataFrames: Final_Arima_Results2, Final_LSTM_Results2, Final_Prophet_Results2
cols = ["Total Energy Consumption(10000 tons of SCE)",
"Proportion of Coal(%)",
"Proportion of Petroleum(%)",
"Proportion of Natural Gas(%)",
"Proportion of Primary Electricity and Other Energy(%)",
"Consumption of Coal(10000 tons)",
"Consumption of Coke(10000 tons)",
"Consumption of Crude Oil(10000 tons)",
"Consumption of Gasoline(10000 tons)",
"Consumption of Kerosene(10000 tons)",
"Consumption of Diesel Oil(10000 tons)",
"Consumption of Fuel Oil(10000 tons)",
"Consumption of Natural Gas(100 million cu.m)",
"Consumption of Electricity(100 million kwh)"]
individual_forecasts = [Final_Arima_Results2, Final_LSTM_Results2, Final_Prophet_Results2]
forecast_names = ["ARIMA", "LSTM", "Prophet"]
for col in cols:
forecast_columns = [forecast[col] for forecast in individual_forecasts]
average_forecast_col = Average2[col]
rmse_values = []
for i, forecast_col in enumerate(forecast_columns):
# Drop rows with NaN from both forecast_col and average_forecast_col
valid_indices = ~np.isnan(forecast_col) & ~np.isnan(average_forecast_col)
forecast_col_valid = forecast_col[valid_indices]
average_forecast_col_valid = average_forecast_col[valid_indices]
# Calculate the RMSE for the current forecast method
rmse = np.sqrt(mean_squared_error(forecast_col_valid, average_forecast_col_valid))
rmse_values.append(rmse)
print(f"RMSE between Average and {forecast_names[i]} forecast for '{col}': {rmse:.2f}")
RMSE between Average and ARIMA forecast for 'Total Energy Consumption(10000 tons of SCE)': 8022.84 RMSE between Average and LSTM forecast for 'Total Energy Consumption(10000 tons of SCE)': 20774.90 RMSE between Average and Prophet forecast for 'Total Energy Consumption(10000 tons of SCE)': 12837.13 RMSE between Average and ARIMA forecast for 'Proportion of Coal(%)': 0.52 RMSE between Average and LSTM forecast for 'Proportion of Coal(%)': 0.55 RMSE between Average and Prophet forecast for 'Proportion of Coal(%)': 0.82 RMSE between Average and ARIMA forecast for 'Proportion of Petroleum(%)': 0.66 RMSE between Average and LSTM forecast for 'Proportion of Petroleum(%)': 0.39 RMSE between Average and Prophet forecast for 'Proportion of Petroleum(%)': 0.99 RMSE between Average and ARIMA forecast for 'Proportion of Natural Gas(%)': 0.87 RMSE between Average and LSTM forecast for 'Proportion of Natural Gas(%)': 1.23 RMSE between Average and Prophet forecast for 'Proportion of Natural Gas(%)': 0.36 RMSE between Average and ARIMA forecast for 'Proportion of Primary Electricity and Other Energy(%)': 1.34 RMSE between Average and LSTM forecast for 'Proportion of Primary Electricity and Other Energy(%)': 1.72 RMSE between Average and Prophet forecast for 'Proportion of Primary Electricity and Other Energy(%)': 0.38 RMSE between Average and ARIMA forecast for 'Consumption of Coal(10000 tons)': 24450.76 RMSE between Average and LSTM forecast for 'Consumption of Coal(10000 tons)': 19965.74 RMSE between Average and Prophet forecast for 'Consumption of Coal(10000 tons)': 44415.29 RMSE between Average and ARIMA forecast for 'Consumption of Coke(10000 tons)': 1432.95 RMSE between Average and LSTM forecast for 'Consumption of Coke(10000 tons)': 4855.01 RMSE between Average and Prophet forecast for 'Consumption of Coke(10000 tons)': 3448.60 RMSE between Average and ARIMA forecast for 'Consumption of Crude Oil(10000 tons)': 2297.26 RMSE between Average and LSTM forecast for 'Consumption of Crude Oil(10000 tons)': 3213.70 RMSE between Average and Prophet forecast for 'Consumption of Crude Oil(10000 tons)': 919.03 RMSE between Average and ARIMA forecast for 'Consumption of Gasoline(10000 tons)': 647.20 RMSE between Average and LSTM forecast for 'Consumption of Gasoline(10000 tons)': 428.36 RMSE between Average and Prophet forecast for 'Consumption of Gasoline(10000 tons)': 226.15 RMSE between Average and ARIMA forecast for 'Consumption of Kerosene(10000 tons)': 440.83 RMSE between Average and LSTM forecast for 'Consumption of Kerosene(10000 tons)': 578.95 RMSE between Average and Prophet forecast for 'Consumption of Kerosene(10000 tons)': 141.36 RMSE between Average and ARIMA forecast for 'Consumption of Diesel Oil(10000 tons)': 3111.66 RMSE between Average and LSTM forecast for 'Consumption of Diesel Oil(10000 tons)': 436.44 RMSE between Average and Prophet forecast for 'Consumption of Diesel Oil(10000 tons)': 2680.33 RMSE between Average and ARIMA forecast for 'Consumption of Fuel Oil(10000 tons)': 99.77 RMSE between Average and LSTM forecast for 'Consumption of Fuel Oil(10000 tons)': 155.57 RMSE between Average and Prophet forecast for 'Consumption of Fuel Oil(10000 tons)': 172.39 RMSE between Average and ARIMA forecast for 'Consumption of Natural Gas(100 million cu.m)': 262.10 RMSE between Average and LSTM forecast for 'Consumption of Natural Gas(100 million cu.m)': 1062.84 RMSE between Average and Prophet forecast for 'Consumption of Natural Gas(100 million cu.m)': 815.29 RMSE between Average and ARIMA forecast for 'Consumption of Electricity(100 million kwh)': 846.29 RMSE between Average and LSTM forecast for 'Consumption of Electricity(100 million kwh)': 1262.58 RMSE between Average and Prophet forecast for 'Consumption of Electricity(100 million kwh)': 436.45
from sklearn.metrics import mean_squared_error
import numpy as np
# Assuming you have calculated your Average2 DataFrame
# And you have individual forecast DataFrames: Final_Arima_Results2, Final_LSTM_Results2, Final_Prophet_Results2
cols = ["Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)",
"Total Energy Consumption, Manufacturing(10000 tons of SCE)",
"Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)",
"Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)"]
individual_forecasts = [Final_Arima_Results1, Final_LSTM_Results1, Final_Prophet_Results1]
forecast_names = ["ARIMA", "LSTM", "Prophet"]
for col in cols: # Assuming 'cols' contains the forecasted column names
# Select the corresponding columns from individual forecasts and the average forecast
forecast_columns = [forecast[col] for forecast in individual_forecasts]
average_forecast_col = Average1[col]
rmse_values = []
for i, forecast_col in enumerate(forecast_columns):
# Drop rows with NaN from both forecast_col and average_forecast_col
valid_indices = ~np.isnan(forecast_col) & ~np.isnan(average_forecast_col)
forecast_col_valid = forecast_col[valid_indices]
average_forecast_col_valid = average_forecast_col[valid_indices]
# Calculate the RMSE for the current forecast method
rmse = np.sqrt(mean_squared_error(forecast_col_valid, average_forecast_col_valid))
rmse_values.append(rmse)
print(f"RMSE between Average and {forecast_names[i]} forecast for '{col}': {rmse:.2f}")
RMSE between Average and ARIMA forecast for 'Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)': 83.17 RMSE between Average and LSTM forecast for 'Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)': 30.72 RMSE between Average and Prophet forecast for 'Total Energy Consumption, Agriculture, Forestry, Animal Husbandry and Fishery(10000 tons of SCE)': 54.36 RMSE between Average and ARIMA forecast for 'Total Energy Consumption, Manufacturing(10000 tons of SCE)': 2146.82 RMSE between Average and LSTM forecast for 'Total Energy Consumption, Manufacturing(10000 tons of SCE)': 4705.46 RMSE between Average and Prophet forecast for 'Total Energy Consumption, Manufacturing(10000 tons of SCE)': 5529.21 RMSE between Average and ARIMA forecast for 'Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)': 1290.92 RMSE between Average and LSTM forecast for 'Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)': 2896.80 RMSE between Average and Prophet forecast for 'Total Energy Consumption, Manufacture of Computers, Communication and Other Electronic Equipment(10000 tons of SCE)': 1610.87 RMSE between Average and ARIMA forecast for 'Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)': 255.99 RMSE between Average and LSTM forecast for 'Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)': 215.48 RMSE between Average and Prophet forecast for 'Total Energy Consumption, Wholesale, Retail Trade and Hotel, Restaurants(10000 tons of SCE)': 415.02